{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "012a51ba-0da1-40b0-b444-6bb14fbf62db",
   "metadata": {
    "jupyter": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 数据加载成功\n",
      " Logistic Regression 训练完成\n",
      "\n",
      "逻辑回归评估:\n",
      "准确率: 0.7488\n",
      "AUC: 0.8356\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          健康       0.97      0.75      0.84     58367\n",
      "         心脏病       0.23      0.78      0.35      5592\n",
      "\n",
      "    accuracy                           0.75     63959\n",
      "   macro avg       0.60      0.76      0.60     63959\n",
      "weighted avg       0.91      0.75      0.80     63959\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/1 - Loss: 0.8318\n",
      "MLP 训练完成\n",
      "\n",
      "MLP评估:\n",
      "准确率: 0.7224\n",
      "AUC: 0.7952\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          健康       0.97      0.72      0.83     58367\n",
      "         心脏病       0.20      0.73      0.32      5592\n",
      "\n",
      "    accuracy                           0.72     63959\n",
      "   macro avg       0.58      0.73      0.57     63959\n",
      "weighted avg       0.90      0.72      0.78     63959\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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Wrl07GT16tOzfv18OHz5sK/Djj7355ptWV/rzzz9br1LPWFKRGQUAAHDKv2xQf1U+nLNQoUIJnmfNmtWa3Os2YsQIadq0qe3TzKmnGX7FihWlV69elunUuk9t/dSjRw8ba9GihcycOdP2Kc2QtmnTxh5rbers2bPtPXVlv7aGqlKlim8fLU7X5cNn+huArmILKd9VgtL5VhsBIGUY+9ELbl8CgGQ4HRMtj9cvJydPnvzH+snrHhfUGSBB6TM6fv64C2fk7HdDHPnMu3btku3bt1tvUA1K44uMjLQMaL169S6r/Vy7dq2cOnXKxjztoZSuqNeMqy5k0tZOviIzCgAA4JRkNqj/Rw6eU1sxxW/HFF/ZsmVtuxKtHb2S4OBgC2yTi5pRAAAAuIbMKAAAQADVjKY2gfvJAAAAkOKRGQUAAEhDNaMpDZlRAAAAuIbMKAAAgFOoGfVZ4H4yAAAApHhkRgEAAJxCzajPyIwCAADANWRGAQAAnELNqM8C95MBAAAgxSMzCgAA4BRqRn1GMAoAAOAYP03TS+BOZgfuJwMAAECKR2YUAADAKUzT+4zMKAAAAFxDZhQAAMDRzKg/WjsFSaAiMwoAAADXkBkFAABwCk3vfRa4nwwAAAApHplRAAAAp7Ca3mdkRgEAAOAaMqMAAABOoWbUZ4H7yQAAAJDikRkFAABwCjWjPiMzCgAAANeQGQUAAHAKNaM+IxgFAABwCtP0PgvcMBsAAAApHplRAAAAhwQFBdnmhxNLoCIzCgAAANeQGQUAAHAImVHfkRkFAACAa8iMAgAAOEUTmP5IYgZJwCIzCgAAANeQGQUAAHAINaO+IzMKAAAA15AZBQAAcAiZUd+RGQUAAIBryIwCAAA4hMyo78iMAgAAwDVkRgEAABxCZtR3ZEYBAADgGjKjAAAATuEOTD4jGAUAAHAI0/S+Y5oeAAAAriEYBQAAcDCB6cmOOruJz06cOCFr1qyR48ePS0pGMAoAABBgZs6cKUWKFJEuXbpIoUKF7Ll6+umnEwS5JUqU8L5my5YtUq1aNcmRI4f07dtX4uLivGPLly+XMmXKSO7cuSUiIiLBe82aNUsKFy4sBQoUkOnTp/t8rQSjAAAADgnSf/yRGZWkp0ZPnjwpPXr0kBUrVsjmzZtl1KhRFlyqdevWydy5cy1bqtuGDRts/9mzZ6VFixZSpUoVOyYyMlImTpxoY0eOHJGWLVtKhw4dZNWqVTJ16lRZunSpN4Dt2LGjDBw4UBYsWCCDBg2SHTt2+PR3RjAKAAAQQKKiouSdd96RChUq2PPKlSvLsWPH5MKFC7J161apW7euZM+e3bZs2bLZMfPmzbMgVrOexYsXl6FDh8r48eNtTINPzXpqwFmyZEkLOD1j48aNk/r161sGtnz58tKzZ0+ZPHmyT9dLMAoAAOAQ/9SLBvm0Qj88PNyyler8+fMyYsQIad26tWVJL126JJUqVZJMmTJJkyZNZO/evXbcxo0bpWbNmpI5c2Z7roGsZkc9Yxpweq6hevXqsn79eu9YgwYNvO8dfyypCEYBAABSUdYzKt6m0+tXo4Fivnz5ZP78+TJy5EgLLkuXLm2Zy02bNkn69OmlW7du3vMWLVrU+1oNPNOlS2dT+YnHQkND5cCBA1d8XfyxpCIYBQAAcLrpvT82+SvrGRYW5t2GDRt21UvR7ObChQttal2n0TVbqvWgt912m+0bPXq0fPvttxZQamAaEhKS4PUZM2aU2NjYy8Y8+9W1xpKKpvcAAACpxL59+yz76JE4gIxPs5u6IOmTTz6xOlBt9aR1oh558uSxafuDBw9Kzpw5bTFSfNHR0ZIhQwYb00VMifera40lFZlRAAAAp/irXjTor9SoBqLxtysFo9qGybN6XmlwqOd45ZVXZNq0ad79ujI+ODjYsq3a0kmfe+zatctKADTYTDymK/ALFixoj681llQEowAAAAGkVKlSMnbsWNs0k9q/f3+56667LEs6YMAAWbx4sU3fd+/eXR566CFbtKQr7HW6fsKECXYOXU3fqFEjqxvVtk4rV66URYsW2YKo4cOHS+PGje24tm3byowZM2xxVExMjNWmesaSiml6AACAFH5v+iAfzpk/f35rRP/MM89Inz59LDicNGmS3HjjjdbaSQNIDTI7depkQaen9lPbNGkvUc2qasZ02bJlNqaN7nVFftOmTSVr1qw21e/pQVqxYkXp1auXVK1a1epFtRZVe5z69Nni4rfXR5Lpbw9aOBxSvqsEpfOtNgJAyjD2oxfcvgQAyXA6Jloer1/O+mLGr59MCXFBzgc+luAMf7VHctKlc7Hy57RH/f6ZDx06ZK2ZtM1Trly5Eozp1P327dulTp06FpTGpyv19+/fL/Xq1fO5ZpTMKAAAQABlRv8NbQXVrFmzK45pC6f4bZziK1u2rG3JQTAKAADglHhtmBw/b4BiARMAAABcQ2YUAADAIal9mt4NZEYBAADgGjKjAAAADiEz6jsyowAAAHANmVEAAACHkBn1HZlRAAAAuIbMKAAAgEPIjPqOzCgAAABcQ2YUAADAKdyByWdkRgEAAOAaMqMAAAAOoWbUd2RGAQAA4BoyowAAAA4hM+o7MqMAAABwDZlRAAAAh5AZ9R3BKAAAgFNo7eQzpukBAADgGjKjAAAADmGa3ndkRgEAAOAaMqMAAAAOITPquzSZGd27d6989913bl8GAABAmpfqgtHhw4fLJ598IhcvXvTu+/PPP+XIkSMSGhoqMTExtl26dOmq51i9erW0adMmwTnOnTsnFy5c8Pv1wx2z3+8hnVrUsMePtqktvy18XaJ+fFcWjusl+XKHJji2WHhu2b/szSue52pjvTs3ks2zB8m+JW/IiBfvk8wZM/jpkwBpS/SJP6V3q9py5MC+y8Y+fW+oRDz7SIJ965cvlOda1ZaHaxaVAQ80kf27frnied966kH57uuZ3udfjXtXejauLF3r3iwjej9q7wskR5D+E+SHTciMphizZs2Sm266SUqVKiWZMmWSL774Qp555hlp0aKFnD59Wpo0aSKFChWSn3/+2fua/PnzS6VKleT222+3beTIkXL06FGpW7euPa9du7bcdtttMmnSJFc/G/yj/d1V5a7aZe1xrUrFZFCPZvLYwElSpvlg+wEf9mxr77FFCuaSr97rITnDslx2nquNPdz6NnnygTvkkf6fSINHIqTaLYXlvZfaX4dPBgQ2DQg12Dx6hUB07y/bZPGsydLpuZe9+w7/vlvGvfqc3NfzRXl37o+Sr3BR+XjI85e99od5X8rm1cu9z7f/d42s+fZreenDmTJk6nxLZkwb8aofPxmAVFszunv3blm7dq3Ur19fduzYIeHh4dKgQQNp1KiRZUVz584t33//vT0/f/68ZTrTp09vQevHH38sZcqUscdKg5DFixdLxowZ3f5Y8KMcoZllWO82smPXIXte/KY88tSQGbJ0zQ57Pmn2anm2c0Pv8Z+/210+/nylDOv9d4D6T2Mdm9eQkVOWyLqte+z5ax/MlUnDEmZrAPhuVP8n5bbGrWTnlg0J9muwOGHoi9K4QxfJU6iwd/+BXb/KfU++KDXubGHPG7R9UCKeeTjBa2NOnpDp7wyR/IWLe/f9tvUnqVi7vuQv8tc+fc9FM0lOIHmoGQ3wzOiHH37ofbxmzRq59dZbZdeuXVKlShW55ZZb5MSJE/anTsO3bNlS9uz5KzjQgFT/T9TjK1SoYFlSVaNGDXt84403St++fV37XPCfN3q3kTlLN8qPm3fb88lzVsvXyzZ5x0sVySO/7j3ifd7m6Q/ki0UJv/j+aSxX9iyy7+Bx7/OLF+Pk4qU4hz8JkPY89tKbclf7Ry/bv+TzKbLv1+2Su0Ah+e/yhXLh/Dnbf2udRlK/TUfvcYf27JS8NxVN8Nrp77wmVeo3luK33OrdV7BYKVm/bIH88fseifrzqCyf86ncUuN2v342AKkwGNW60ClTpkj27NntudaNNm7c2ALMH374QZYvXy5hYWGWGa1ataosXLhQihf/67fc4OBgC1AHDhwo/fv3lxdffNH29+nTxx5XrlxZdu7cKZ9//rmrnxHOqlu1pNSvXkpeeuerq2ZNH2t7u4yb9b13354Dx656vquN/bRtnzS/o7z3+YMta8iS1dv/1bUDELmx4E2X7TsTe0q+HBsheQreJMcO/i4Lpo+XIV3ayrkzZxIcpwHqvKkfSYM2nbz7Itf9IFvXrpT7n+qf4FjNiuYpWFj6tK5jdaNnY09J885P+vGTIU3cgckfW4BKNdP0kZGR0rlzZwtIdYHSzJkzbdPM6L59++TAgQMSFRUlzZs3t+PTpUvnfa1O2Wtm9OzZsxaYepw5c8YWMemmx+gipqvR1+rmoe+FlCskQ3p5f0AHeXropxIT+/f/b/G90+8+Wb3xN1m4MvJfvdfg97+Wr97vIUsmPCtZM2eU8qUKSqNHR/yrcwK4snVL58nZ07HS74NPJVv2nHLxwgXp3+EuWfnN5wmyol98GCEhGTNLvXv+qt8+d/aMTBjWTx5+8XXJlCVrgnP+uHiuHDu8X4Z9ttjO+enIofLBoF7y9PC/Z+MA+E+qCUY9i480GM2aNavVj+pipmPHjkmzZs0kLi5O7r33Xnn55Zetnijxavu77rpLWrVqJSEhId79H3zwgQWp2upJA90OHTpc9f2HDRsmr7zyit8/J5zRr+vdsn7rHpn//dYrjndsUUPqVi0lNe4f9q/fa9+h41Kl3etSumheeb3XPXL4WJSs3LDzX58XwOX+/OOQFC9f2YJGlS59egkvcbMtXvKIXLtSFs2aJIM//krSp7/B9s0eP1KKlakolW7/u0bcY9X8r6y+tGDRkva843ODpXv9W+RU9EnJki3sun02BAZqRgM4GI1v6dKlMnr0aFvAVK9ePTl06JAFoxqEeh7nyZPHjj158qRlUjUL2r17d+nRo4f3XxStFZ0/f76sX7/eAtZr6devn/Tu3TtBZlTfHynT/XdXkdw5ssnBFcPtubZaantnZal6S2FbtBTxfDtp98xY+ePPaMfeMzrmjDSoUVrqPxzh2DkBJJQzTz7LcsZ37NB+KVmhij0+sn+vjB7wlHR+/jWrBfVYtWC2RB8/ZkGmOnvmtPy46P9k59af7Lsj6s+/y3BOHv2rjvzSxau3CASQxoNRbcn0yCOPWJ2oBpd33nmnrYrX1k5vvPGGHVOnTh0ZM2aMNbcvV66cZMuWTaZPny7Hjx+X/fv32zGFCxeWbt262WKot99++5rvqRnV+FlVpGyNHn1H0qX7uyTjjd6t5cdNu2Xq/62RNZ/2k4hPFsl/I/dIlkx/9QM9dfrqJRpJ9ULXJrbAaeOO3//1uQBcWcXaDWXyW4NlyeeTpdLtjWzafu8vkdJz2BirG9VWUJXr3ilV7mhi9aUqJFNmGTB2lly8+Hcv6RnvDpHit1SWOi3utX6j30z5UHLmySs3hGSUBdM/tuA2W/YcLn5SpFZkRtNIMKr1oBqEagCp7Zw2b95swWi+fPlky5YtlhnVTKgaN26c9SDVLGavXr1s1bxO5esU/ZtvvmlT/LrQqVatWm5/LDho/x8nEjzXutGjJ2LkviZVJf+NYfLyky1s88h0a89/9X7aDP/+JlVtuh6A/2iA+Ny7E2X6u6/LtBGvSfbceaTn0NGSK18Ba3ivTe51W/bVdO9r3p69Um4skHAmKyRTFpvq1+3O+x+WPw8fsKn86BPHpUT5ytJ10H9c+HQIBBoz+iNuDArcWFSC4jRyS0U0qPz1119tFb2untdpeM2Gak2oNrvXZvZK9+sU/KOPPiq//PKLDB061Kb333nnHes/qguddEGSBq2aZdVV+E888YQMGDAgwSKnq9Fpen3/kPJdJSgdd9sBUqOxH73g9iUASIbTMdHyeP1y9l2vfcZTAk9cULTnLAkOyez4+S+djZVd77dLUZ85zbV28oiOjpbBgwdbY/tVq1bJe++9Zw3t9a5MOgWv/wfpAid9XrNmTRk/frwUKFBAnn/+eWuY//vvv1tWVes/tf+oHqur8idOnGg9R5MSiAIAAFw9M+qPW4JKwEp10/Ta2F5XyntqJxo2bGibh45p5lMTvjp1r3WhSoNTpVlQ3RLTnqUAAAC4vlJdMJq4h+iVxq41DgAA4Dd+qhmVAM6MMicNAAAA16TKzCgAAEBKRGsn35EZBQAAgGvIjAIAADiEPqO+IzMKAAAA15AZBQAAcEhwcJBtTovzwzlTCjKjAAAAcA2ZUQAAAIdQM+o7MqMAAABwDZlRAAAAh9Bn1HdkRgEAAByepvfH5qsTJ07ImjVr5Pjx45KSEYwCAAAEmJkzZ0qRIkWkS5cuUqhQIXuutmzZItWqVZMcOXJI3759JS4uzvua5cuXS5kyZSR37twSERGR4HyzZs2SwoULS4ECBWT69OkJxkaNGiV58+aVYsWKyZIlS3y+VoJRAAAAh6fp/bEl1cmTJ6VHjx6yYsUK2bx5swWLGniePXtWWrRoIVWqVJF169ZJZGSkTJw40V5z5MgRadmypXTo0EFWrVolU6dOlaVLl3oD2I4dO8rAgQNlwYIFMmjQINmxY4eN6fM+ffrI2LFjZcqUKRb8Hjt2THxBMAoAABBAoqKi5J133pEKFSrY88qVK1uAOG/ePAtUNetZvHhxGTp0qIwfP96O0eBTs54acJYsWdICTs/YuHHjpH79+hZoli9fXnr27CmTJ0+2sTFjxkjnzp2lVatWUqtWLfvzyy+/9Ol6CUYBAABSSWY0KioqwabZzsTCw8Mtk6nOnz8vI0aMkNatW8vGjRulZs2akjlzZhvTYFWzo0rHNOD0vE/16tVl/fr13rEGDRp4z5/UsaQiGAUAAEglwsPDJSwszLsNGzbsqsdqoJgvXz6ZP3++jBw50oLXokWLesc18EyXLp0tcEo8FhoaKgcOHLDHyR1LKlo7AQAApJKm9/v27bOAzyMkJOSqr9HM58KFC+XZZ5+1KXadmk98fMaMGSU2NlbSp0+fYMyzXyV3LKnIjAIAAKQSoaGhCbZrBaOa+dTFSp988ol88cUXkjNnTluoFF90dLRkyJDhsjHPfpXcsaQiGAUAAHBIkPipZlSSnm7VFk26et5Dg0M9h7Zt0pXyHrt27bKaUw0otd1T/LENGzZIwYIF7XFyx5KKYBQAACCAlCpVylot6abT+v3795e77rpLmjZtajWeEyZMsON0NX2jRo2sblTbOq1cuVIWLVpki56GDx8ujRs3tuPatm0rM2bMsDZRMTExVn/qGWvXrp2MHj1a9u/fL4cPH7YV+J6xpCIYBQAAcEhKuANT/vz5rUn9u+++K+XKlbMazkmTJll9p7Zp0tZM2th+9uzZ8uabb9pr9LmuuteAVRvYax/RAQMG2FjFihWlV69eUrVqVct6avCqfUyV9i1t2LChtYPShUy33nqrtGnTxre/s7j4rfeRZPqbha5iCynfVYLS+VYbASBlGPvRC25fAoBkOB0TLY/XL2c9M+Mv5kkJcUGFfnMkXcYsjp//4plTsmlYS0c+86FDh6z9krZ5ypUrV4Ixnbrfvn271KlTR7JmzZpgTNtAaQa0Xr16l9WFrl27Vk6dOmVjvjToV6ymBwAAcIivd0tKKifPqe2emjVrdsUxzW7Gb9UUX9myZW27Eq0dTS6m6QEAAOAaMqMAAACppM9oICIzCgAAANeQGQUAAEhDNaMpDcEoAACAQ5im9x3T9AAAAHANmVEAAACHME3vOzKjAAAAcA2ZUQAAAKf4qWZUAjcxSmYUAAAA7iEzCgAA4BBqRn1HZhQAAACuITMKAADgEPqM+o7MKAAAAFxDZhQAAMAh1Iz6jswoAAAAXENmFAAAwCHUjPqOzCgAAABcQ2YUAADAIdSM+o5gFAAAwCEEo75jmh4AAACuITMKAADgEBYw+Y7MKAAAAFxDZhQAAMAh1Iz6jswoAAAAXENmFAAAwCHUjPqOzCgAAABcQ2YUAADAIdSM+o7MKAAAAFxDZhQAAMAhmr/0S82oBC4yowAAAHANmVEAAACHBAcF2eaP8wYqMqMAAABwDZlRAAAAh9Bn1HcEowAAAA6htZPvmKYHAACAa8iMAgAAOCQ46K/NH+cNVGRGAQAA4BoyowAAAE6xBUx0vfcFmVEAAAC4hswoAACAQ2jt5DsyowAAAHANmVEAAACHBP3vH3+cN1CRGQUAAIBryIwCAAA4hD6jviMzCgAAANeQGQUAAHAI96b3HZlRAACAADN79mwpVqyYpE+fXipVqiTbtm2z/U8//bQ3YNatRIkS3tds2bJFqlWrJjly5JC+fftKXFycd2z58uVSpkwZyZ07t0RERCR4r1mzZknhwoWlQIECMn36dJ+vlWAUAADA4T6j/tiSaufOnfLII4/IG2+8Ifv375dSpUpJly5dbGzdunUyd+5cOX78uG0bNmyw/WfPnpUWLVpIlSpV7JjIyEiZOHGijR05ckRatmwpHTp0kFWrVsnUqVNl6dKl3gC2Y8eOMnDgQFmwYIEMGjRIduzYIb4gGAUAAAgg27Zts0D0vvvuk7x588oTTzxhQeeFCxdk69atUrduXcmePbtt2bJls9fMmzdPTp48aVnP4sWLy9ChQ2X8+PE2psGnZj014CxZsqQFnJ6xcePGSf369S3YLV++vPTs2VMmT57s0/USjAIAADgkOCjIb5uKiopKsGlGM7HmzZtLt27dvM81U6lB5ObNm+XSpUs2bZ8pUyZp0qSJ7N27147ZuHGj1KxZUzJnzmzPK1SoYNlRz5gGnJ661erVq8v69eu9Yw0aNPC+V/yxpCIYBQAASCXT9OHh4RIWFubdhg0bds3rOXfunLz99tvSvXt3Cy5Lly5tmctNmzZZPaknaNXAtmjRovE+R5CkS5fOpvITj4WGhsqBAweu+Lr4Y0nFanoAAIBUYt++fRbweYSEhFzz+MGDB0uWLFlsGv2GG26w+k6P0aNHWyCpAaUGponPlTFjRomNjb1szLNfXWssqQhGAQAAUklrp9DQ0ATB6LUsWbJERo0aJatXr7ZANLE8efLYtP3BgwclZ86cthgpvujoaMmQIYON6SKmxPvVtcaSiml6AACAALNr1y5b/a7BaNmyZW2ftmuaNm2a9xhdGR8cHGxT/9rSSZ/Hf73Wo2qwmXhMF0MVLFjQHl9rLKkIRgEAAAKotdPp06dtEVOrVq2kdevWEhMTY5suShowYIAsXrxYFi5caHWkDz30kC1a0hX2Ol0/YcIEO4eupm/UqJHVjWpbp5UrV8qiRYvk/PnzMnz4cGncuLEd17ZtW5kxY4YtjtL3GDlypHcsqZimBwAACCALFy60xUq6ffTRRwmynffff78FkBpkdurUyYJOT+2ntmnSbKpmUDVjumzZMhvTRvcjRoyQpk2bStasWa0llKcHacWKFaVXr15StWpVqxfVVfs9evTw6XqD4uK310eS6W8PuootpHxXCUrnW20EgJRh7EcvuH0JAJLhdEy0PF6/nPXFTGr95PWKC1qPWSE3ZMrq+PnPn46RL5+o6/fPfOjQIWvNpG2ecuXKlWBMg9nt27dLnTp1LCiNTwNfbbBfr149n2tGk5QZjd9b6p8KZQEAAJA65cuXT5o1a3bFMV15H7+NU3xal+qpTfVVkoLRhx9+OFknBwAASEs0def8WnrxyzlTiiQFo507d/b/lQAAACDNYQETAABAKukzGoho7QQAAIDUE4xqfyltA1CjRg1rarp161apXr267Ny50z9XCAAAkEoEB/lvC1Q+B6PaO+qzzz6TRx991G75pI1Sa9WqJY8//rh/rhAAAAABy+ea0VmzZsm6deukePHi8uKLL1rT1BdeeEFKlSrlnysEAABIJagZvQ6ZUb1/6YoVKxL85ehU/dX6TgEAAKQlbt4KNE1kRvV+pPfcc4+MHTtWYmNjpXfv3vLdd9/JpEmT/HOFAAAACFg+B6NNmjSRLVu2WN3orbfeKoUKFZI333xTihUr5p8rBAAASCWYpr9OfUZLlCgh/fv3T85LAQAAgOTXjJ4+fdoWLmlAmiVLFsuI6lS9rqwHAABIy2jtdB2C0W7dusnixYttan7ZsmUSEREhq1evtv0AAACAX6fp58yZI+vXr7fMqKpWrZrccsst9icAAEBaRs3odciMVq1aVX744YcE+3788UepVKlSMt4eAAAAaVmSMqN6tyWPbNmyySOPPCIffPCB9Rbdt2+fBaeNGzf253UCAACkeJq/9EcOM0jSeDBauHDhBI+1pZOH3nmpYcOG/rk6AAAABLQkBaODBw/+x2POnTvnxPUAAACkWsFBQbb547yByucFTIcOHZIhQ4bIjh075OLFi7YvLi5Otm3bZmMAAACA3xYwPfjgg/Lnn39K5syZJVOmTHLvvfdaYNqjRw9fTwUAABBQ/HFf+qAAvz+9z8Go9hQdMWKE9O3b1xrdP/HEEzJ+/HiZP3++f64QAAAAAcvnYLRgwYKycOFC6ysaGRlpd2TSPqObN2/2zxUCAACksj6j/tgClc81o3rnpQ4dOkiTJk2kRYsW1ndUa0Zvv/12/1whAAAAApbPwWirVq3kwIED1m907NixMm3aNJuu79y5s3+uEAAAIJXwV31nUOAmRn0PRlX27Nntz3Tp0hGEAgAA/A+tna5DzSgAAADgamYUAAAAl2Oa3k/BqN6DPimruH777bdkXAIAAADSqiQFoxMnTvT/lQAAAKRy/mrDFBTAqdEkBaP16tXz/5UAAAAgzaFm9F/au+w/Ehoa6vZlAEiG6NPn3b4EAMkQHZVZUvLKcH+sDg+WwBXInw0AAAApHJlRAAAAh1Az6jsyowAAAHANmVEAAACHaAIzmD6jPiEzCgAAANeQGQUAAHBIsJ8yo8FkRgEAAIAUlhk9d+6c3HDDDRIXFyfBwcS1AAAgbWM1ve98jiCjo6OlW7dukjdvXsmcObNs3rxZChUqJOvXr0/G2wMAAATeNL0/tkDlczD6yCOPyO7du+WTTz6RLFmySFhYmDz77LPy5JNP+ucKAQAAELB8nqZftGiRZUPDw8Ntal7Txp07d5ZXX33VP1cIAACQSuhsuj9m1IPIjP7t5ptvlokTJyaoi1ixYoWUK1fOH9cHAACAAOZzZvS9996Tpk2bygcffGD1o/fdd5/s3btX5syZ458rBAAASCWCg4Js88d5A5XPwWi1atXk119/lf/7v/+T/fv32+KlZs2aWe0oAAAA4NdgVLOgqk6dOt59J0+etO2mm27y9XQAAAABVf/oj2aXwRK4fA5GixQpYnWi2ls0cc+rixcvOnltAAAACHA+B9qXLl2yoFP/jImJkWXLlskdd9whX331lX+uEAAAIJWtpvfH5ovZs2dLsWLFJH369FKpUiXZtm2b7d+yZYuVXObIkUP69u1ryUWP5cuXS5kyZSR37twSERGR4HyzZs2SwoULS4ECBWT69OkJxkaNGmX95/X9lixZItc165spUyabrv/666/llVde+TenAgAAgAN27txpfeHfeOMNW99TqlQp6dKli5w9e1ZatGghVapUkXXr1klkZKS3Q9KRI0ekZcuW0qFDB1m1apVMnTpVli5d6g1gO3bsKAMHDpQFCxbIoEGDZMeOHTamz/v06SNjx46VKVOm2PscO3bs+pcg6JsePnzYiVMBAACkWsHy12p6xzdJempUs6AaiGrHI81YPvHEE7JhwwaZN2+erfHRrGfx4sVl6NChMn78eHuNBp+a9dSAs2TJkhZwesbGjRsn9evXt0CzfPny0rNnT5k8ebKNjRkzxvrNt2rVSmrVqmV/fvnllz7+nfmoaNGilob1bFpDWqJECYukAQAA4K7mzZvbrds9NIupAebGjRulZs2adjt3VaFCBcuOKh3TgNOzHqh69ereW73rWIMGDbznS+qY3xYwedK5HnrRBQsWtAgbAAAgLfP3HZiioqIS7A8JCbHtas6dOydvv/229O7d21pzalLx73MGSbp06eT48eN23rJly3rHQkND5cCBA973jP+6pI75LRitV6+ery8BAACAA8LDwxM8Hzx4sLz88stXPV7Hs2TJYlPsAwYMuCxwzZgxo8TGxtpCp/hjnv0quWN+C0Y//PBDK3DNnz+/ry8FAAAIaMFBf23+OK/at2+fZR89rpUV1ZXtutJ99erVcsMNN0jOnDltMVJ8ejfNDBky2JguYkq8XyV3zG81oyNGjLAiWAAAAFw+ne6PBUxB/wtGNRCNv10tGN21a5et59Fg1DP9ri2ddKV8/GN0hb0GlInHNNbTMswrvS6pY34LRnWV1ZAhQ6zHKAAAAFKW06dP2yImXdneunVri9l003acWuM5YcIEO05X0zdq1MjqRnXWe+XKlbJo0SI5f/68DB8+XBo3bmzHtW3bVmbMmCGbN2+284wcOdI71q5dOxk9erS1kNLOSroC3zPmt2l6LX5VuoJel/JrHYKHtgEAAABIq/y9gCkpFi5caKvkdfvoo48SZEK1TZNmTLXhfXBwsN28SGmje539btq0qWTNmlWyZ8/uXbResWJF6dWrl1StWtVqQnVlfo8ePWxM+5bOnDnT9qmGDRtKmzZtxBdBcfFb7yeBNlG94omCguTjjz+WtEJ/swgLC5PDx04mqN0AkHpEnz7v9iUASIboqCgpXii39cxMKd/Bnrig/1f/lYxZsjl+/jOnomXoPZUd+cyHDh2y9kva5ilXrlwJxjRg3b59u2VRNSiNT4NbzYDqYvbEdaFr166VU6dO2Vji28U7HoziLwSjQOpHMAqkTik5GB0w23/B6JBWzgSjKU2SakZXrFhh96IHAAAArnswqh35WbAEAABwbUF+/CdNB6M6k+/r/D8AAADg2Gp6XVX1T8HqxYsXk3o6AACAgOPvpvdpOhhdvny5ZMvmfEEuAAAA0q4kB6MVKlQIuNVbAAAATiIz6qeaUe3Unzlz5mScHgAAAPiXmVG90xIAAACuTdfQ+GPRd1AALyT3+d70AAAAgFN8vjc9AAAAroyaUd+RGQUAAIBryIwCAAA4REs7/VHeGRTAmVGCUQAAAIcEBwXZ5o/zBiqm6QEAAOAaMqMAAAAOYQGT78iMAgAAwDVkRgEAAJzipwVMQmYUAAAAcB6ZUQAAAIcES5Bt/jhvoCIzCgAAANeQGQUAAHAITe99R2YUAAAAriEzCgAA4BD6jPqOzCgAAABcQ2YUAADAIdyb3ndkRgEAAOAaMqMAAAAOYTW97whGAQAAnGx6749pegncaJRpegAAALiGzCgAAIBDmKb3HZlRAAAAuIbMKAAAgINZPn9k+oIlcAXyZwMAAEAKR2YUAADAIUFBQbb547yBiswoAAAAXENmFAAAwCGav/RHDjNIAheZUQAAALiGzCgAAIBD9O5LfrkDU1Dg5kbJjAIAAMA1ZEYBAAAcFLg5TP8gMwoAAADXkBkFAABwCPem9x2ZUQAAALiGzCgAAIBDuAOT7whGAQAAHJxy9se0c7AErkD+bAAAAEjhyIwCAAA4hGl635EZBQAACEBHjx6VokWLyu7du737nn76aW/ArFuJEiW8Y1u2bJFq1apJjhw5pG/fvhIXF+cdW758uZQpU0Zy584tERERCd5n1qxZUrhwYSlQoIBMnz7d5+skGAUAAHBIkB83XwPR5s2bJwhE1bp162Tu3Lly/Phx2zZs2GD7z549Ky1atJAqVarYMZGRkTJx4kQbO3LkiLRs2VI6dOggq1atkqlTp8rSpUu9AWzHjh1l4MCBsmDBAhk0aJDs2LHDp2slGAUAAAgw7du3lwceeCDBvgsXLsjWrVulbt26kj17dtuyZctmY/PmzZOTJ09a1rN48eIydOhQGT9+vI1p8KlZTw04S5YsaQGnZ2zcuHFSv3596dKli5QvX1569uwpkydP9ulaCUYBAAAcEn8K3OnNFx999JFNyce3efNmuXTpklSqVEkyZcokTZo0kb1799rYxo0bpWbNmpI5c2Z7XqFCBcuOesY04PRcQ/Xq1WX9+vXesQYNGnjfI/5YUhGMAgAApBJRUVEJNp1evxKtFU1Mg8vSpUtb5nLTpk2SPn166datm/e88V+jgWe6dOlsKj/xWGhoqBw4cOCKr4s/llSspgcAAEglfUbDw8MT7B88eLC8/PLLSTqH1nbq5jF69GgLJDWg1MA0JCQkwfEZM2aU2NjYy8Y8+9W1xpKKYBQAACCV2Ldvn2UfPRIHkL7IkyePTdsfPHhQcubMaYuR4ouOjpYMGTLYmC5iSrxfXWssqZimBwAASCU1o6GhoQk2X4JRbdc0bdo073NdGR8cHGzZVm3ppM89du3aZSUAGmwmHtMV+AULFrTH1xpLKoJRAACANKBixYoyYMAAWbx4sSxcuFC6d+8uDz30kC1a0hX2Ol0/YcIEO1ZX0zdq1MjqRrWt08qVK2XRokVy/vx5GT58uDRu3NiOa9u2rcyYMcMWR8XExMjIkSO9Y0nFND0AAIBDktMTNCmcOGenTp2stZMGkBpk6nMNOj21n9qmSXuJagZVM6bLli2zMW10P2LECGnatKlkzZrVWkJ5epBqgNurVy+pWrWq1Ytq66cePXr4dF1BcfHb6yPJ9LeHsLAwOXzsZILaDQCpR/Tp825fAoBkiI6KkuKFcltfzJTyHeyJC6au/FkyZ/2rd6eTYmOipWPtUn7/zIcOHbLWTNrmKVeuXAnGdOp++/btUqdOHQtKE6/U379/v9SrV8/nmlEyowAAAA7R0k5/3EY+6Drdmj5fvnzSrFmzK47pyvsrtYxSZcuWtS05CEYBAAAcEixBtvnjvIGKBUwAAABwDZlRAAAAh6T2aXo3kBkFAACAa8iMAgAAOCTof//447yBiswoAAAAXENmFAAAwCHUjPqOzCgAAABcQ2YUAADAwdpOf/QEDaJmFAAAAHAemVEAAACHUDPqOzKjAAAAcA2ZUQAAAIeQGfUdmVEAAAC4hswoAACAQ7gDk+8IRgEAABwSHPTX5o/zBiqm6QEAAOAaMqMAAAAOYZred2RGAQAA4BoyowAAAA6htZPvyIwCAADANWRGAQAAHKIJTP/UjAYuMqMAAABwDZlRAAAAh9Bn1HdkRgEAAOAaMqMAAAAOoc9oKs6Mbty48bq9V1RUlLz++usSExNz3d4TAAAAKTQY1UC0Ro0asm3btmS9PjY21l7/xx9/JOn4TJkyydChQ+X777/37rt48aJcuHAhWe+P1OPo0aNyc8mismf3bu++3s88LZluCPJu5W4u4R3bumWL1K5ZTfLfmEP6vdBX4uLivGPtWrdM8LqmjRtd988DpEX3t24uM6ZOssc/fL9CalctLzcXyS9j3n/He4z+rPZ9tqeUuimvlAi/UZ7q/picPn3axvRxntAMl2179/z93wXg3/YZ9ccWqFJEMFqxYkXp0KGDPPzwwxYUXokGjwUKFJAiRYpI3rx5ZfHixd6xzJkzS7t27eTbb7+VS5cuydmzZxO8tk2bNnZM9uzZbbvxxhvlhhtukPbt23v36TZhwgS/f1a4G4i2adU8QSCq/rt+nXw5Z64cPHLcttVrN9h+/feobesWUrlyFVm5ep1s3xYpkz+Z6H3d+vXrZN2Gzd7Xzfxi9nX/TEBaM+vTabJ08UJ7fPToEXmwfRtp3e5++WbRCvn8s+ny/YplNvbZ9Cmy85efZfH3P8qc+Utkx7ZIefftN23szYj35Je9f3i36bPmSLHiJaRgoXBXPxuQVqWIYFS98sor8ttvv8l///vfK45rkNqtWzfZvXu3FC1a1Pa99dZbkjFjRgskddr9ySeflLCwMLn11lttfODAgbJmzRrJkiWLDBs2TE6cOHHVLTo6Wrp27XpdPzOur4c6tpf72z+QYJ9mw7dFbpXb69T1/lKSLVs2G1swf55EnTwpb/4nQooVLy6vvDZUJk4Yb2P79+/X1IuUu+UW7+v03zMA/nP8zz9l8EsvSImSpez5559Ol3z58stzL7wkxUqUtD+nTvorqbBh/Tpp3qqNhN9UWMqWKy93N28pu3/baWOanAjLnt27fTBqpPTtN1DSpUvn6udDIPUZ9c8WqFwPRjVYnD59utx0002yd+9eqVatmu1PnN0MDk54qfofDc1u9ujRw4LJ9evXy5gxYyyojIyMtGOaNGliGVMNZFesWGEBQ6FChSy7Gh4eLnny5LHHuuXPn98ytAhcoz/4SJ586ukE+7Zs3mzZ9BpVK0mObJmkZbMm9u+h2rxpo1SvUdO+uFT5ChUsO6rWrf3R/r0qXqSQ5ArLIg92bC/Hjx934VMBacfgl56Xpi1aSZVqNez51i2bpHadOyTof/OXlatUk00//ZXQKF2mrGVR//jjsOzbu0e++vwzqdfg8lIaDVp1el6zqwDSUDCq0+lVq1aVM2fOyJIlS2TUqFHeWk6Pxo0bW3AZP/h89dVXJX369BbAevZ5gtTt27fL6NGjE7xP7dq17djQ0FC54447LGj9/fffLbuqU/KaydLHuh08ePCai6g0ONaFT/E3pC5F/pdRj2/btkgpVaq0jJ8wWdb+d5P9+9XziW42pv8fFyny92v0C0//ndOg8+cd26V8hYry5ey5svz71bJ71y4Z9FK/6/p5gLREp9+/W75UBr86zLsvOjpKbipSxPs8a7ZQOXTooD3u1PlROXUqRm4pES5VbikpNxUuIvc/8OBl5x334Sh5uEu3yxIeQHIFS5AEB/lhk8DNjbry07dhwwYLRHWKXTcNABLr2bOn9O3bV3bu/GtaRbNXgwYNsmlVXaykNDOl2VEd0yDBM8Wizz20zlQl/g/N+fPn7b2TSqf5tQTAs2lmFalfhwc6yso166TmbbdJiZIl5Z33RsviRd9aIKr/XmYICUlwfEjGjLZgru8L/WTu/G+lQsWKckv58jLszbfkyy9mufY5gECm3xd9evWQ4RHvSdb/ldEo/RkNyfD3z6j+N/10bKw9HjvmPQkLyy7/3fqrbIjcKRcuXJSXB7x42bT//G++lg4dO1/HT4NAxzR9KglGN23a5J2O90yvJKbT6w0aNLBFTRpcavCYmO4LCQmRe+65Rx5//HFZu3atlCpVyrsQSVfnP/XUUzJ58mQLWkuXLm1T9blz55Z7773XAl19rJv+R2zEiBFXveZ+/frJyZMnvdu+ffsc+/tAyqGlG/rv26GDByVHzpxy9MiRBOMx0dGSIUOGy15344155NixY5eVlwD49yKGvy6VKleVO5s0TbA/e46ccuzokSv+fOpipid79ZZC4TfZwqSXXn5Npk1OuEh17tdfSc3bbpfsOXJcp08CIMUEo8uWLfNmN69FFyj9+eeftljk1KlT8vbbb1vgqPWhSqdLs2bNKnPmzJEPP/zQAtyff/5ZHnvsMRufOHGiZUs1qNVFKRq4Llq0yFZV62IorVPdsmWLPdeV9VcKMjz0tTrdH39D6qftmmZMn+Z9vmb1KsuiFwoPl6pVq8maNau8YzoVr8Fmzpw5pdMD98vKeK3B9HXa5UH/PQHgrC9mfmoZTG3RpNsXM2fIC72fks+mTZa1a/8q21KbN/0k+fL/NRumv1TG/2Xyj8OHL+vWMvuLWdKs5T3X8ZMgTSA1mvLvwKRZUQ0ub7/99n88VjOZmzdvtuBgz549MmXKFGnVqpXUrFnTxo8cOWKZ0Kt54403LPNap04dm1b3TNVrZlPPpdlRrStdunSp7WclZdpToUJFeWXwAAsk9Yuq9zNPScdOD9miJV1hHx0VJZMmTpCHHn5Ehr8xVBo0bGT/ntxyS3l5vs+zMvw/I+TYsaMyaEA/6fr4E25/HCAgaWum+H2gXx7wgi1iat/xIbm1TDFZvnSx1Lq9rrz/zttSv+FddoxmPEdGvGU/r+fOnZP3RvxHGt/d3HsO7Tm6auUKeeud9135TABcDEZLlCgh33zzjZQrV+6qx+h/JJo1ayaTJk2y1e9KM57FixdPcNxPP/0knTp1uup5NBDVWiM9TlfKaxNkrfdr27atPdeFU7169bLa1PiLp5B2dOjYSSIjt0qH+9ral1b7BzrJq0OGeuvRRn84Tjp36iD9X+xrv8wsWPRXD8Pn+r5gmdJWzZtY1r1b9x7y/Iv9Xf40QGAqUPCv7wGPLFmySq5cuW17ddh/5IF2LW1faPbsMvKDcXZMv4Gv2AKnVwf2k5iYaLmj4Z3y+vAI7znWrlklYdlzSJGixa7750Fg43agqSAY1YzT3Xff7X2uX/CaKdUeo55pcm3D9N1339kUvNLbdu7YsUOKFfvrPxoaVOoUvbZwKlu27GX1pBrMeoLLTz/91KbvNWDQaRut68uVK5e3PnT48OFWAqA1qknJ1iL1O33+77soqddeH2bblTRv0VK27tgpG/673to86b87SmuQP/hovG0Arq/3Pvj75+7hx7pJ/YZ3yq8/75AatW73fm9o/9BRY69+I5O6dzSQrb9S+w+kyWA0sVq1akn//v0TZD01QNUFSbrYSE2dOtWm7JXWf2qdp7aHat68uXz88ccSERFhwYHyZFK1hZMGrVp3+tJLL9k+rffT6XoNUD20xk/rRbV8wNNiCogvX758cnfTZm5fBoCrKFykqG1AiuCvW3cGScAKiot/s+0USnuR6jR99+7dZe7cuTbFrwuINMtZsmTJa7720KFDdvvPa9WDHj582No1+dLqSVv/6GsOHzvJYiYglYo+fXmXDgApn9bzFy+U29aApJTvYE9csPinvdbz1mkx0VHSsNJNKeozB0xmNCk0G6qb0lpSD13VnJSs1j/RxSsAAAD/lr8WvgdJ4OKWEwAAAHBNqsiMAgAApAqkRn1GZhQAAACuITMKAADgEPqM+o7MKAAAAFxDZhQAAMAhQX7qMxoUuIlRglEAAACnsH7Jd0zTAwAABKCjR49K0aJFZffu3d59ehdLvU16jhw5pG/fvna3So/ly5dLmTJlJHfu3HZ3y/hmzZolhQsXlgIFCsj06dMTjOkdLLVnu962XW9U5CuCUQAAAKdTo/7YfAxE9bbp8QNRvS16ixYtpEqVKrJu3TqJjIyUiRMn2tiRI0ekZcuW0qFDB1m1apXdin3p0qXeALZjx44ycOBAWbBggQwaNEh27NhhY/q8T58+MnbsWJkyZYp06dLF7pDpC4JRAACAANO+fXt54IEHEuybN2+e3U5Us57FixeXoUOHyvjx421Mg0/NemrAqbda14DTMzZu3DipX7++BZrly5eXnj17yuTJk21szJgx0rlzZ2nVqpXUqlXL/vzyyy99ulaCUQAAAIdbO/njHxUVFZVg02znlXz00Ufy9NNPJ9i3ceNGqVmzpmTOnNmeV6hQwbKjnjENOIP+t1KqevXqsn79eu+Y57bsvowlFcEoAABAKhEeHi5hYWHebdiwYVc8TmtFE9PgNf5+DTzTpUsnx48fv2wsNDRUDhw4cMXXJXUsqVhNDwAAkEpaO+3bt88CPo+QkJAknyN9+vSXHZ8xY0aJjY29bMyz/0qvS+pYkq/Lp6MBAADgmtDQ0ATBqC9y5sxpi5Hii46OlgwZMtiYLmJKvN/zuuSMJRXT9AAAAIG1mP6KtKWTrpT32LVrl9WcakCZeGzDhg1SsGDBK74uqWNJRTAKAACQBtStW9dqPCdMmGDPdTV9o0aNrG5U2zqtXLlSFi1aJOfPn5fhw4dL48aN7bi2bdvKjBkzZPPmzRITEyMjR470jrVr105Gjx4t+/fvl8OHD9sKfM9YUjFNDwAAkAZuwZQ+fXpr06S9RLXhfXBwsCxbtszGtNH9iBEjpGnTppI1a1bJnj27twdpxYoVpVevXlK1alWrCdXWTz169LAx7Vs6c+ZM26caNmwobdq08e2jxcVvvY8k098sdBXb4WMnk127AcBd0afPu30JAJIhOipKihfKbT0zU8p3sCcu+H7r75I1m/PXFBMdJbeXK+TIZz506JC1X9I2T7ly5UowplP327dvlzp16lhQGp+2gdIMaL169S6rC127dq2cOnXKxjztoZKKzCgAAIBD4vcEdfq8TsmXL580a9bsimPapulKbaFU2bJlbbsSrR1NLmpGAQAA4BoyowAAAKmkz2ggIhgFAAAI/PVLKRbT9AAAAHANmVEAAACnkBr1GZlRAAAAuIbMKAAAQBpq7ZTSkBkFAACAa8iMAgAAOITWTr4jMwoAAADXkBkFAABwCIvpfUdmFAAAAK4hMwoAAOAUUqM+IzMKAAAA15AZBQAAcAh9Rn1HZhQAAACuITMKAADgEPqM+o7MKAAAAFxDZhQAAMAhLKb3HcEoAACAU4hGfcY0PQAAAFxDZhQAAMAhtHbyHZlRAAAAuIbMKAAAgFP81NpJAjcxSmYUAAAA7iEzCgAA4BAW0/uOzCgAAABcQ2YUAADAKaRGfUZmFAAAAK4hMwoAAOAQ+oz6jswoAAAAXENmFAAAwCFBfuozGhS4iVEyowAAAHAPmVEAAACHsJjedwSjAAAATiEa9RnT9AAAAHANmVEAAACH0NrJd2RGAQAA4BoyowAAAE6WjPqjtZMELjKjAAAAcA2ZUQAAAIewmN53ZEYBAADgGjKjAAAADuF2oL4jMwoAAADXkBkFAABwDFWjviIzCgAAANeQGQUAAHAINaO+IzMKAAAQYJ5++mkJCgrybiVKlLD9W7ZskWrVqkmOHDmkb9++EhcX533N8uXLpUyZMpI7d26JiIhIcL5Zs2ZJ4cKFpUCBAjJ9+nRHr5VgFAAAwOGKUX9svli3bp3MnTtXjh8/btuGDRvk7Nmz0qJFC6lSpYqNR0ZGysSJE+34I0eOSMuWLaVDhw6yatUqmTp1qixdutQbwHbs2FEGDhwoCxYskEGDBsmOHTvEKQSjAAAAAeTChQuydetWqVu3rmTPnt22bNmyybx58+TkyZOW9SxevLgMHTpUxo8fb6/R4FOznhpwlixZ0gJOz9i4ceOkfv360qVLFylfvrz07NlTJk+e7Nj1EowCAAA4XDPqj01FRUUl2DTbmdjmzZvl0qVLUqlSJcmUKZM0adJE9u7dKxs3bpSaNWtK5syZ7bgKFSpYdlTpmAacOqWvqlevLuvXr/eONWjQwHv++GNOIBgFAABwSJAf/1Hh4eESFhbm3YYNGyaJaYBZunRpy15u2rRJ0qdPL926dbPgtWjRot7jNPBMly6dTeMnHgsNDZUDBw7Y42uNOYHV9AAAAKnEvn37LBj0CAkJuewYre/UzWP06NEWTOripMTHZ8yYUWJjYy1gjT/m2a+uNeYEglEAAIBU0vM+NDQ0QTCaFHny5LFp+3z58tlipPiio6MlQ4YMkjNnTlvElHi/utaYE5imBwAACCB9+/aVadOmeZ/r6vjg4GBbfKSPPXbt2mU1pxpsarun+GO6+r5gwYL2+FpjTiAYBQAACKDWThUrVpQBAwbI4sWLZeHChdK9e3d56KGH5K677rL6zwkTJthxupq+UaNGVjeqbZ1WrlwpixYtkvPnz8vw4cOlcePGdlzbtm1lxowZtjAqJiZGRo4c6R1zAtP0AAAAAaRTp07W2kmDSA009bkGnlr7qW2atJeoZk81W7ps2TJ7jTa6HzFihDRt2lSyZs1q7aA8PUg1uO3Vq5dUrVrV6kW19VOPHj0cu96guPit95Fk+puFrmI7fOykz7UbAFKG6NPn3b4EAMkQHRUlxQvltp6ZKeU72BMX/Pr7Ucnmh2uKjoqSEg595kOHDllrJm3zlCtXrgRjOnW/fft2qVOnjgWliVfp79+/X+rVq+dozSiZUQAAgDQkX7580qxZsyuO6ar7+G2c4itbtqxtTiMYBQAAcEj8nqBOnzdQsYAJAAAAriEzCgAAkEr6jAYiMqMAAABwDZlRAAAAh5AY9R2ZUQAAALiGzCgAAIBDgoL+2vxx3kBFMAoAAOAY/7R2kgCeqGeaHgAAAK4hMwoAAOAQpul9R2YUAAAAriEYBQAAgGsIRgEAAOAaakYBAAAcQs2o78iMAgAAwDVkRgEAABztMup8GjOIPqMAAACA88iMAgAAOISaUd+RGQUAAIBryIwCAAA4RBOY3JneN2RGAQAA4BoyowAAAE4hNeozglEAAACH0NrJd0zTAwAAwDVkRgEAABxCayffkRkFAACAa8iMAgAAOIT1S74jMwoAAADXkBkFAABwCqlRn5EZBQAAgGvIjAIAADiEPqO+IzMKAAAA15AZBQAAcAh9Rn1HMJpMcXFx9md0VJTblwIgmWJOn3f7EgAkQ3R0dILv4pQkyk9xQVQAxxsEo//yB6FE0XC3LwUAgDT7XRwWFiYpQYYMGSRfvnxS0o9xQb58+ex9Ak1QXEr8tSIVuHTpkhw4cECyZcsmQYGcO0+j9DfQ8PBw2bdvn4SGhrp9OQB8xM9wYNPQRQPRAgUKSHBwyln+cubMGTl37pzfzp8hQwbJmDGjBBoyo8mk//IXKlTI7cuAn+mXGF9kQOrFz3DgSikZ0fg0UAzEYNHfUs6vEwAAAEhzCEYBAADgGoJR4ApCQkJk8ODB9ieA1IefYSD1YAETAAAAXENmFAAAAK4hGAUAAIBrCEYBAADgGoJRAAAAuIZgFLiG+Ov7vvvuOzl69Kir1wPAGRcvXvTeMUc3AO4hGAWuwXOr11dffVVGjRolWbJkcfuSADgQiKZLl07+/PNPad26tRw5ckQuXLjg9mUBaRbBKHAFly5dkhMnTtjjt956S7Zu3SoTJ06U/fv3y4EDB+xLDEDqc/r0aQtEY2NjpUaNGlK9enV73rx5c7ufPd0OgeuPe9MDieiX0caNG6V79+5Srlw5y5hMmjRJXnnlFfn000+lQYMGUqVKFXn00Uclffr0EhzM73RAarBnzx554YUXLAB9//33pVu3btK1a1epU6eOPPLIIwnuYa//HfDMjADwL75FgUT0C6h06dLeILRp06Yyfvx4mT9/vgWhv/zyi+zatUsyZMhAIAqkotmOwoULS7FixaRPnz7y9NNPyxNPPCF33HGH/eLZt29fO2bu3LkSHR1NIApcR9yBCbhCJiQiIkK2bdsmd955p3To0EGKFy9u0/R//PGH9O7dW26++WYZOHCgVKtWzbKjAFJ+jagGmTo136NHD2nfvr3NchQpUkTmzJljgWilSpXseM2e3nPPPdSIA9cJaR3gfzyB6IgRI+SHH36Q9957z2pDS5QoIb/++qvMnj1b1qxZY4Hp0KFD5ccff/SuwtUgFUDKo0GmBqJaD1q5cmV58MEH7RdMzYjqDIjOcnz44YdSq1YtqVq1qjz55JM2C/LVV19Z8ArA/0jpAPFoRnTkyJGydu1a6d+/vyxfvlzGjh0rixcvltdff10KFChgGVFd/DB8+HALTD///HPb/+yzz0ru3Lnd/ggA4tFSGl20pHWhWnLTs2dPqxnV7OhTTz1lgejzzz9vWdGPP/5Yzp8/L1u2bJGVK1fKDTfcIPfdd5/bHwEIeGRGgXhuuukmm9LT1fP6WOvIzp49a61f9MtKV9Pr6vpvv/3WFjvo1P3x48elfv36TOkBKYynCk1nMTQ7qj/TGpR26dLFAlGlv2zmyZNHfv/9d6lbt67VhWfNmtXKb8aMGWO/eALwLzKjSLPi14hq5iRTpkzSrl07268LG7TVS9GiRW3x0uHDh2X9+vXy8ssvy5AhQ2xKb/DgwW5/BADX4Pn5rl27tv1s79ixw+pEte5baYa0YsWKNguiv1AOGjTIakh1xuO1116T7NmzS+bMmV3+FEDgYwET0qxz587ZingNKrU29M0335RDhw7Zaltd0KBfWM8995xNvWtvUc2aaGAaEhJidaWLFi2yLzZFGxggZdCfRU+dqNKZC/3Z1tkMXbyk0/BaKxoTEyPvvPOObN68WcLDw+3nu1WrVvYzr3bv3i033nijzXjw8w34F9P0SLO0x6BO223atMmC0QoVKsgbb7xhYy1btrRFSi+99JLVnGlNqNaV/d///Z9N8TVs2FBWr17tnQbkiwpIGXTRkScQ1UznwYMHbUZjwYIFMnr0aGnRooVMmDDBfvbvuusua+uk0/j58+e3GRKlP9eaIfWU3vDzDfgXwSjSLP0y0tXw+fLls8yI0toxz5eRLlzQqTrtR6ircL/++mtr6bR3715b2NC2bVu+pIAUJDIy0hYpffnll3ZTCp3JyJs3r2TMmNG7uFBrwrUOfNWqVfL4449LmTJl7Of8gQcekDZt2tgx/FwD1xc1o0gz4k+1aWNrbcek0/JaF6rT9bqCvlGjRvblpPVjminRNi///e9/Zfr06VK2bFmbttNjtGY0W7Zsbn8kAP+jU/P6M6oLknr16iW//fabzJw5U3LlyuUd11kO/Znv3LmzLVLSBYraMUM7Y7z44ouyZMkStz8GkCYRjCLN8ASiOlWnK+E/+OADy4zcdttt9sWlNWPaxF6D0Z07d8rSpUulY8eOcsstt8gXX3wh/fr1s+l5nQLULzIAKYfnbmhTpkyxGu9PPvnEglH92e7UqZN89tlnlvm8/fbbbSZEFydpvai2ZBs3bpwtWtJfQAFcf0zTI03QRQvffPON/Oc//7E+g1oLqqvjtV2TfmndeuuttmJe27ucOnXKVszrIibNpupK3Bw5cljmNCwszM7HNB7gPp1y9/ypm5bSaOs17ReqQaf+XH/00UdSsmRJ+fnnn20GpHz58haIKp3K1zZu2rrt4Ycftn2s6QWuP4JRpAk61a7N6XWKXafrtOG1BpuPPPKI9RLULyldnKQZFb1NoE7Bf/rppxZ0btiwwe7GpCtxAaQMnhXzuir+/vvvlxkzZlhvUK3x1sVKOh2vdaAnT5602vBy5cp5X+ehbZ10RsTzS6biF03g+iMYRZqggaR+CenKWaWLHDRTotPuercVvTWgtnrSaTrNonqm/LT+THuO6q0E+ZICUg79GdVAVNuraeCpi5U0O3r33Xfbqnj9BVI7YDRp0kTeffddGTVqlE3H6+s0+7lixQr7pVPbPukvowDcQ80o0szCJf0y0h6i2vx61qxZ1n9Qa0d1MdKFCxds8YJO0e/bt8+O0ak9PUZX3erUHoCUQ3+5fOWVV+zx999/bzMd2iNUS3Hq1asn69ats9ZMzzzzjDWuP3HihEybNs0ea2snnarXrCgA95EZRcDTQFQDUm1WrxlRXYykU3nz58+XefPm2TS8BqfaP/SHH36w43Q6XwPRZcuWMT0PpECa4dRfJHXTRYkjR460Dhm6Ml5/udSFiHr7Xi230d6jWh+qP+PaIUM7aOjiJgApA5lRpJmAVOvLdMpdMyobN260wHTgwIGWCdWsit4qsFChQpZN0QUNixcvtpX0AFKm1q1b28+1Zjx1Zbz+Avndd9/ZIsVhw4bZjSz0F86aNWtacBoREWEr6rX8Jn67JwDu4nagSFO0lkyzJSVKlLBeg5ox0UypfoF16NDBFjbpYgidwvP0JwSQsiS+Pafe6nP27Nk2i6GtnLSJvT7WOzDpwiYNVPVnWxcyvv/++/YaAlEg5SAzijQlU6ZM1k9QaUsnrQ299957bcquatWqlkHxjANwX+KgMX4gqgsLte77zjvvtP2a8dRfLHW2Q0tsNBOqsyG6wEmn7glEgZSJYBRpjn6Reb6MtFZUM6Hab1TvR69TeQBSDl3tvmvXLildurRNses945X2CtZymwIFClg/Ub0970MPPWS36tVWTjrTcezYMesfOnjwYPv5Vhq0EogCKQs/kUiT9MvI029QFzjolJ5+eXEHFiBlufnmm21hkrZl0sVHnkb3umJeA8w9e/ZYxlNXzetzrfvu2rWrHaM3q9DXd+vWTfLly2f7aNEGpDzUjCJNY7oOSF0131pqE59+hWmAqs3tPXXe2je4cOHCdvelyMhIa25PEAqkXHwLI00jEAVSvtjYWOsDrNPvypMd9dSP6p2XtH7UY8CAAbayXnuNagkOgSiQsvFNDABIkTylNDpNP336dOnUqZMcPXrU2jnpmKeHsN5fXnsDa59gpYsRtW0bN6sAUgem6QEAKZpmQrV9k9aGah9gvZta7ty5bb/ObmhQqoubdMX8Z599JhkzZnT7kgH4gMwoACDF0vvKP/jgg3aP+T59+ljdqLZr8mRINRDVm1SsXr3aVtoTiAKpD8EoACDFKlWqlDWtf+edd2xhkq6oP3funN1lSW9aoY91Zb3ebUlvCaqY8ANSF4JRAECKVbt2bWnWrJnMnz9ffv75Z2ncuLG1ajp16pQ91rup3XTTTTauPLWkAFIPakYBACma3lFJewG3atVKxowZY5nPhQsXyscff+ztFaxo1QakTgSjAIAUb/LkydbeSafrw8LCbJ+2evLUiBKIAqkXwSgAINX0G82cOfNl++Pfrx5A6kMwCgAAANcwpwEAAADXEIwCAADANQSjAAAAcA3BKAAAAFxDMAoAAADXEIwCAADANQSjAAAAcA3BKIAkWbZsmTUW1+2GG26QChUqyIIFCxx/jyJFivzjvuth4sSJcscdd/jt+IcfflhefvnlZF6d2Gv1HACQ2hGMAkiy0NBQOX78uOzdu1eeeuopadu2rRw4cMCv73n77bfLpk2bfH6dBrAayAIAUjaCUQBJplnR7NmzS/78+aVr165StGhRWb58uV/fM3369BYEAwACE8EogH8VKJ47dy7BtPOUKVOkdOnSMmbMGO9xa9eulRo1akhYWJi0adNGTp486R0bN26cFCpUyLaFCxde9h5Xm6ZfvHixlQpky5ZN7r77bvn9999tf5MmTSxo3rNnj9SvX98ev/HGG97XzZ8/X8qXL29BdZcuXeTs2bPesddee03y5MkjpUqVkg0bNjjyd/TRRx/JTTfdZNepnz0mJsY7tnv3bqlcubLkyJFDevXqJRcuXPCOTZo0SUqWLCm5c+eW/v372/3XASAQEYwCSJZvv/1Wtm/fLrVr1/bu0xrS999/X/7zn/9Iy5Ytbd+JEycsWNRNp9ujoqLkueees7GNGzdKz549ZdSoUTJv3jyZMWNGkt57165d0qJFC3nmmWckMjLSMqd6HvX5559bKUF4eLh8/fXX9vjZZ5+1sV9//VVatWplgZ8GyD/++KO89dZbNjZnzhwZMWKEzJo1y+o/Naj+t7Zu3SpPPPGEBdx6nceOHZPRo0d7x/XzvvrqqxZw6/uPHTvW9q9YscIC5YiICAu6P/nkE5k6deq/vh4ASInSu30BAFIPzWhqRvHMmTMSEhJigWeJEiW84zt37pRffvnFMqAec+fOtQVPgwcPtixlnz595KGHHrKxr776Sho1amQBotKx4cOH/+N1TJ8+XerUqSOPPvqoPX/77bflp59+ssdZsmSxP4ODgyVr1qx2vR6ffvqpVKpUyQI91aNHD/n4449lwIAB8uWXX8oDDzwgdevWtbHHHnvMgtV/o3jx4nLo0CHJmDGjrFmzRs6fPy87duzwjt9zzz3SvHlze/zkk09aIKzXNHnyZGndurUF3OrBBx+0YLVTp07/6noAICUiGAWQZDrVrEGfBpcFChSw4DK+zp07JwhElU6fHzlyxKai1aVLlyQ6OtoC2oMHD9oUdvzgLSn27dsnxYoV8z73TPP/E70WnX73BKg6La4Bq9JradCgQYJr+bfB6OnTp6229rvvvpOKFStKunTp5OLFi95xrbn10L8HvQbPdS5dutR7nVoKoSUJABCICEYBJJlmG6/VZsmTlYxPg8QqVapYVlJp7aNmWDWg1frM+CvldZV+UugUvAZ4Hj///LPcf//9sn79ertGz7UmrrPUa9Fso2ZSlQaGsbGx9livJX5ngKRey7W8++679lk1yNTP+/zzz8sff/yRIKj20PfOmzev9zoff/xxb3mBZlQ1iAeAQETNKAC/atasmQV2mmXMlCmTTUXrIiMNFDUw1DrTb775xuorPfWb/6RDhw5WV6m1nRrQDRkyxIJJTyDqyWzqgigNBLXuUrVv396CWC0l0DKD9957Tx555BEb0xpXrcv84YcfbEpdFx79W5oB1iBSM8PTpk2zRV3xA+QvvvjCPv/mzZutbtZTrqBlDLNnz7Ypfl0k9tJLL9kGAIGIYBSAX+lUs9Y7ajZSp9ZnzpxpzzXIqlatmi120hrOpk2b2iKnpNDpbQ3WdIFPuXLlbJHUhAkTEhyjga3Wq+r0t6e5vAaoukq9d+/eVuuqWVmtP1XaM1WzkRoQarmBJzD8N3SBla7W19X5en1ah+qpbfUEwP369bM6Va2d1bpRpfWwr7zyitWKlilTxqbp4y98AoBAEhRHvxAAAAC4hMwoAAAAXEMwCgAAANcQjAIAAMA1BKMAAABwDcEoAAAAXEMwCgAAANcQjAIAAMA1BKMAAABwDcEoAAAAXEMwCgAAANcQjAIAAEDc8v+JIznAcn9ilwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3658/3658 - 10s - 3ms/step - accuracy: 0.7819 - loss: 0.4624 - val_accuracy: 0.7906 - val_loss: 0.4156\n",
      "Epoch 2/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8025 - loss: 0.4183 - val_accuracy: 0.7809 - val_loss: 0.4131\n",
      "Epoch 3/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8115 - loss: 0.3989 - val_accuracy: 0.7987 - val_loss: 0.3924\n",
      "Epoch 4/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8170 - loss: 0.3883 - val_accuracy: 0.7986 - val_loss: 0.3904\n",
      "Epoch 5/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8212 - loss: 0.3801 - val_accuracy: 0.7916 - val_loss: 0.4091\n",
      "Epoch 6/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8239 - loss: 0.3744 - val_accuracy: 0.8235 - val_loss: 0.3609\n",
      "Epoch 7/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8257 - loss: 0.3707 - val_accuracy: 0.8153 - val_loss: 0.3694\n",
      "Epoch 8/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8274 - loss: 0.3675 - val_accuracy: 0.8082 - val_loss: 0.3831\n",
      "Epoch 9/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8287 - loss: 0.3644 - val_accuracy: 0.8219 - val_loss: 0.3622\n",
      "Epoch 10/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8304 - loss: 0.3609 - val_accuracy: 0.8174 - val_loss: 0.3692\n",
      "Epoch 11/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8315 - loss: 0.3586 - val_accuracy: 0.8278 - val_loss: 0.3522\n",
      "Epoch 12/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8328 - loss: 0.3563 - val_accuracy: 0.8130 - val_loss: 0.3788\n",
      "Epoch 13/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8335 - loss: 0.3540 - val_accuracy: 0.8354 - val_loss: 0.3404\n",
      "Epoch 14/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8344 - loss: 0.3526 - val_accuracy: 0.8328 - val_loss: 0.3459\n",
      "Epoch 15/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8356 - loss: 0.3498 - val_accuracy: 0.8213 - val_loss: 0.3704\n",
      "Epoch 16/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8368 - loss: 0.3484 - val_accuracy: 0.8317 - val_loss: 0.3447\n",
      "Epoch 17/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8367 - loss: 0.3472 - val_accuracy: 0.8253 - val_loss: 0.3663\n",
      "Epoch 18/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8380 - loss: 0.3449 - val_accuracy: 0.7953 - val_loss: 0.4037\n",
      "Epoch 19/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8384 - loss: 0.3442 - val_accuracy: 0.8200 - val_loss: 0.3656\n",
      "Epoch 20/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8392 - loss: 0.3431 - val_accuracy: 0.8416 - val_loss: 0.3338\n",
      "Epoch 21/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8386 - loss: 0.3430 - val_accuracy: 0.8260 - val_loss: 0.3655\n",
      "Epoch 22/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8401 - loss: 0.3415 - val_accuracy: 0.8250 - val_loss: 0.3544\n",
      "Epoch 23/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8397 - loss: 0.3402 - val_accuracy: 0.8177 - val_loss: 0.3716\n",
      "Epoch 24/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8407 - loss: 0.3394 - val_accuracy: 0.8113 - val_loss: 0.3741\n",
      "Epoch 25/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8401 - loss: 0.3398 - val_accuracy: 0.8150 - val_loss: 0.3741\n",
      "Epoch 26/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8420 - loss: 0.3374 - val_accuracy: 0.8349 - val_loss: 0.3399\n",
      "Epoch 27/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8421 - loss: 0.3365 - val_accuracy: 0.8403 - val_loss: 0.3339\n",
      "Epoch 28/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8423 - loss: 0.3365 - val_accuracy: 0.8054 - val_loss: 0.3850\n",
      "Epoch 29/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8431 - loss: 0.3351 - val_accuracy: 0.8272 - val_loss: 0.3557\n",
      "Epoch 30/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8433 - loss: 0.3335 - val_accuracy: 0.8427 - val_loss: 0.3286\n",
      "Epoch 31/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8435 - loss: 0.3336 - val_accuracy: 0.8394 - val_loss: 0.3340\n",
      "Epoch 32/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8450 - loss: 0.3312 - val_accuracy: 0.8290 - val_loss: 0.3546\n",
      "Epoch 33/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8454 - loss: 0.3311 - val_accuracy: 0.8185 - val_loss: 0.3737\n",
      "Epoch 34/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8456 - loss: 0.3308 - val_accuracy: 0.8171 - val_loss: 0.3739\n",
      "Epoch 35/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8468 - loss: 0.3283 - val_accuracy: 0.8164 - val_loss: 0.3790\n",
      "Epoch 36/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8463 - loss: 0.3283 - val_accuracy: 0.8239 - val_loss: 0.3604\n",
      "Epoch 37/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8473 - loss: 0.3273 - val_accuracy: 0.8638 - val_loss: 0.3001\n",
      "Epoch 38/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8472 - loss: 0.3270 - val_accuracy: 0.8172 - val_loss: 0.3707\n",
      "Epoch 39/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8481 - loss: 0.3258 - val_accuracy: 0.8587 - val_loss: 0.3081\n",
      "Epoch 40/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8480 - loss: 0.3247 - val_accuracy: 0.8480 - val_loss: 0.3253\n",
      "Epoch 41/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8489 - loss: 0.3237 - val_accuracy: 0.8462 - val_loss: 0.3290\n",
      "Epoch 42/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8490 - loss: 0.3229 - val_accuracy: 0.7698 - val_loss: 0.4450\n",
      "Epoch 43/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8488 - loss: 0.3230 - val_accuracy: 0.7914 - val_loss: 0.4101\n",
      "Epoch 44/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8492 - loss: 0.3224 - val_accuracy: 0.8188 - val_loss: 0.3679\n",
      "Epoch 45/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8498 - loss: 0.3211 - val_accuracy: 0.8345 - val_loss: 0.3491\n",
      "Epoch 46/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8498 - loss: 0.3211 - val_accuracy: 0.8313 - val_loss: 0.3462\n",
      "Epoch 47/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8499 - loss: 0.3203 - val_accuracy: 0.7942 - val_loss: 0.4113\n",
      "Epoch 47: early stopping\n",
      "Restoring model weights from the end of the best epoch: 37.\n",
      "\n",
      "训练结束 - 验证准确率: 0.8638, 验证损失: 0.3001\n",
      "\u001b[1m1999/1999\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 825us/step\n",
      "\u001b[1m1999/1999\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 838us/step\n",
      "\n",
      "CNN评估:\n",
      "准确率: 0.8638\n",
      "AUC: 0.6582\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          健康       0.94      0.91      0.92     58367\n",
      "         心脏病       0.30      0.41      0.34      5592\n",
      "\n",
      "    accuracy                           0.86     63959\n",
      "   macro avg       0.62      0.66      0.63     63959\n",
      "weighted avg       0.88      0.86      0.87     63959\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression 测试准确率: 0.7488\n",
      "MLP 测试准确率: 0.7224\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 35797 (\\N{CJK UNIFIED IDEOGRAPH-8BD5}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 19981 (\\N{CJK UNIFIED IDEOGRAPH-4E0D}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 21516 (\\N{CJK UNIFIED IDEOGRAPH-540C}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27169 (\\N{CJK UNIFIED IDEOGRAPH-6A21}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 22411 (\\N{CJK UNIFIED IDEOGRAPH-578B}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30340 (\\N{CJK UNIFIED IDEOGRAPH-7684}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23545 (\\N{CJK UNIFIED IDEOGRAPH-5BF9}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27604 (\\N{CJK UNIFIED IDEOGRAPH-6BD4}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNN 测试准确率: 0.8638\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters for Logistic Regression: {'C': 0.001}\n",
      "Epoch 0/1 - Loss: 0.8616\n",
      "Epoch 0/1 - Loss: 0.8487\n",
      "Epoch 0/1 - Loss: 0.8721\n",
      "Epoch 0/1 - Loss: 0.7606\n",
      "Epoch 0/1 - Loss: 0.7446\n",
      "Epoch 0/1 - Loss: 0.7834\n",
      "Epoch 0/1 - Loss: 0.8358\n",
      "Epoch 0/1 - Loss: 0.8007\n",
      "Epoch 0/1 - Loss: 0.7729\n",
      "Epoch 0/1 - Loss: 0.7848\n",
      "Epoch 0/1 - Loss: 0.8164\n",
      "Epoch 0/1 - Loss: 0.8474\n",
      "Epoch 0/1 - Loss: 0.8518\n",
      "Epoch 0/1 - Loss: 0.7821\n",
      "Epoch 0/1 - Loss: 0.8381\n",
      "Epoch 0/1 - Loss: 0.8000\n",
      "Epoch 0/1 - Loss: 0.8259\n",
      "Epoch 0/1 - Loss: 0.8050\n",
      "Epoch 0/1 - Loss: 0.7713\n",
      "Epoch 0/1 - Loss: 0.8468\n",
      "Epoch 0/1 - Loss: 0.7654\n",
      "Epoch 0/1 - Loss: 0.8070\n",
      "Epoch 0/1 - Loss: 0.7667\n",
      "Epoch 0/1 - Loss: 0.7778\n",
      "Epoch 0/1 - Loss: 0.7757\n",
      "Epoch 0/1 - Loss: 0.7802\n",
      "Epoch 0/1 - Loss: 0.8217\n",
      "Epoch 0/1 - Loss: 0.8248\n",
      "Epoch 0/1 - Loss: 0.7641\n",
      "Epoch 0/1 - Loss: 0.7545\n",
      "Epoch 0/1 - Loss: 0.7868\n",
      "Epoch 0/1 - Loss: 0.8475\n",
      "Epoch 0/1 - Loss: 0.8144\n",
      "Epoch 0/1 - Loss: 0.7838\n",
      "Epoch 0/1 - Loss: 0.7650\n",
      "Epoch 0/1 - Loss: 0.7858\n",
      "Epoch 0/1 - Loss: 0.8688\n",
      "Epoch 0/1 - Loss: 0.7924\n",
      "Epoch 0/1 - Loss: 0.7796\n",
      "Epoch 0/1 - Loss: 0.8051\n",
      "Epoch 0/1 - Loss: 0.7921\n",
      "Epoch 0/1 - Loss: 0.8151\n",
      "Epoch 0/1 - Loss: 0.8085\n",
      "Epoch 0/1 - Loss: 0.8143\n",
      "Epoch 0/1 - Loss: 0.8185\n",
      "Epoch 0/1 - Loss: 0.8192\n",
      "Epoch 0/1 - Loss: 0.7653\n",
      "Epoch 0/1 - Loss: 0.7854\n",
      "Epoch 0/1 - Loss: 0.7417\n",
      "Epoch 0/1 - Loss: 0.7559\n",
      "Epoch 0/1 - Loss: 0.8403\n",
      "Epoch 0/1 - Loss: 0.8231\n",
      "Epoch 0/1 - Loss: 0.8093\n",
      "Epoch 0/1 - Loss: 0.7716\n",
      "Epoch 0/1 - Loss: 0.7833\n",
      "Epoch 0/1 - Loss: 0.8165\n",
      "Epoch 0/1 - Loss: 0.8413\n",
      "Epoch 0/1 - Loss: 0.8191\n",
      "Epoch 0/1 - Loss: 0.8430\n",
      "Epoch 0/1 - Loss: 0.7658\n",
      "Epoch 0/1 - Loss: 0.8366\n",
      "Epoch 0/1 - Loss: 0.7964\n",
      "Epoch 0/1 - Loss: 0.7755\n",
      "Epoch 0/1 - Loss: 0.7555\n",
      "Epoch 0/1 - Loss: 0.8250\n",
      "Epoch 0/1 - Loss: 0.8308\n",
      "Epoch 0/1 - Loss: 0.8067\n",
      "Epoch 0/1 - Loss: 0.8436\n",
      "Epoch 0/1 - Loss: 0.7707\n",
      "Epoch 0/1 - Loss: 0.7829\n",
      "Epoch 0/1 - Loss: 0.7999\n",
      "Epoch 0/1 - Loss: 0.8456\n",
      "Epoch 0/1 - Loss: 0.8443\n",
      "Epoch 0/1 - Loss: 0.8380\n",
      "Epoch 0/1 - Loss: 0.7865\n",
      "Epoch 0/1 - Loss: 0.7639\n",
      "Epoch 0/1 - Loss: 0.8202\n",
      "Epoch 0/1 - Loss: 0.8289\n",
      "Epoch 0/1 - Loss: 0.8334\n",
      "Epoch 0/1 - Loss: 0.8309\n",
      "Epoch 0/1 - Loss: 0.8325\n",
      "Epoch 0/1 - Loss: 0.8002\n",
      "Epoch 0/1 - Loss: 0.8411\n",
      "Epoch 0/1 - Loss: 0.7693\n",
      "Epoch 0/1 - Loss: 0.8095\n",
      "Epoch 0/1 - Loss: 0.7810\n",
      "Epoch 0/1 - Loss: 0.7806\n",
      "Epoch 0/1 - Loss: 0.8331\n",
      "Epoch 0/1 - Loss: 0.8178\n",
      "Epoch 0/1 - Loss: 0.7862\n",
      "Epoch 0/1 - Loss: 0.7943\n",
      "Epoch 0/1 - Loss: 0.7863\n",
      "Epoch 0/1 - Loss: 0.8180\n",
      "Epoch 0/1 - Loss: 0.8004\n",
      "Epoch 0/1 - Loss: 0.8069\n",
      "Epoch 0/1 - Loss: 0.7943\n",
      "Epoch 0/1 - Loss: 0.7674\n",
      "Epoch 0/1 - Loss: 0.8165\n",
      "Epoch 0/1 - Loss: 0.8088\n",
      "Epoch 0/1 - Loss: 0.7499\n",
      "Epoch 0/1 - Loss: 0.8017\n",
      "Epoch 0/1 - Loss: 0.8158\n",
      "Epoch 0/1 - Loss: 0.7922\n",
      "Epoch 0/1 - Loss: 0.7494\n",
      "Epoch 0/1 - Loss: 0.8016\n",
      "Epoch 0/1 - Loss: 0.7992\n",
      "Epoch 0/1 - Loss: 0.7941\n",
      "Epoch 0/1 - Loss: 0.7790\n",
      "Epoch 0/1 - Loss: 0.8131\n",
      "Epoch 0/1 - Loss: 0.8344\n",
      "Epoch 0/1 - Loss: 0.8074\n",
      "Epoch 0/1 - Loss: 0.8714\n",
      "Epoch 0/1 - Loss: 0.8455\n",
      "Epoch 0/1 - Loss: 0.7714\n",
      "Epoch 0/1 - Loss: 0.7641\n",
      "Epoch 0/1 - Loss: 0.7668\n",
      "Epoch 0/1 - Loss: 0.8716\n",
      "Epoch 0/1 - Loss: 0.8143\n",
      "Epoch 0/1 - Loss: 0.7864\n",
      "Epoch 0/1 - Loss: 0.7503\n",
      "Epoch 0/1 - Loss: 0.8637\n",
      "Epoch 0/1 - Loss: 0.7716\n",
      "Epoch 0/1 - Loss: 0.8416\n",
      "Epoch 0/1 - Loss: 0.7397\n",
      "Epoch 0/1 - Loss: 0.7848\n",
      "Epoch 0/1 - Loss: 0.7684\n",
      "Epoch 0/1 - Loss: 0.8229\n",
      "Epoch 0/1 - Loss: 0.9190\n",
      "Epoch 0/1 - Loss: 0.7913\n",
      "Epoch 0/1 - Loss: 0.8154\n",
      "Epoch 0/1 - Loss: 0.8258\n",
      "Epoch 0/1 - Loss: 0.8083\n",
      "Epoch 0/1 - Loss: 0.7723\n",
      "Epoch 0/1 - Loss: 0.7976\n",
      "Epoch 0/1 - Loss: 0.7652\n",
      "Epoch 0/1 - Loss: 0.7802\n",
      "Best parameters for MLP: {'alpha': 0.01, 'hidden_layer_sizes': (128, 64), 'learning_rate_init': 0.01}\n",
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3658/3658 - 10s - 3ms/step - accuracy: 0.7834 - loss: 0.4605 - val_accuracy: 0.7809 - val_loss: 0.4320\n",
      "Epoch 2/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8057 - loss: 0.4105 - val_accuracy: 0.7796 - val_loss: 0.4170\n",
      "Epoch 3/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8152 - loss: 0.3920 - val_accuracy: 0.7775 - val_loss: 0.4397\n",
      "Epoch 4/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8200 - loss: 0.3825 - val_accuracy: 0.8153 - val_loss: 0.3690\n",
      "Epoch 5/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8230 - loss: 0.3757 - val_accuracy: 0.7816 - val_loss: 0.4172\n",
      "Epoch 6/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8243 - loss: 0.3724 - val_accuracy: 0.7788 - val_loss: 0.4350\n",
      "Epoch 7/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8254 - loss: 0.3697 - val_accuracy: 0.7524 - val_loss: 0.4733\n",
      "Epoch 8/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8277 - loss: 0.3655 - val_accuracy: 0.8191 - val_loss: 0.3625\n",
      "Epoch 9/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8292 - loss: 0.3631 - val_accuracy: 0.8196 - val_loss: 0.3663\n",
      "Epoch 10/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8306 - loss: 0.3604 - val_accuracy: 0.8270 - val_loss: 0.3519\n",
      "Epoch 11/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8323 - loss: 0.3572 - val_accuracy: 0.8348 - val_loss: 0.3429\n",
      "Epoch 12/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8328 - loss: 0.3563 - val_accuracy: 0.8232 - val_loss: 0.3551\n",
      "Epoch 13/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8338 - loss: 0.3535 - val_accuracy: 0.8170 - val_loss: 0.3711\n",
      "Epoch 14/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8349 - loss: 0.3515 - val_accuracy: 0.7496 - val_loss: 0.4840\n",
      "Epoch 15/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8359 - loss: 0.3500 - val_accuracy: 0.7480 - val_loss: 0.4769\n",
      "Epoch 16/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8358 - loss: 0.3486 - val_accuracy: 0.8309 - val_loss: 0.3477\n",
      "Epoch 17/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8363 - loss: 0.3483 - val_accuracy: 0.8379 - val_loss: 0.3409\n",
      "Epoch 18/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8371 - loss: 0.3471 - val_accuracy: 0.8043 - val_loss: 0.3834\n",
      "Epoch 19/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8382 - loss: 0.3458 - val_accuracy: 0.8326 - val_loss: 0.3504\n",
      "Epoch 20/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8382 - loss: 0.3444 - val_accuracy: 0.8247 - val_loss: 0.3601\n",
      "Epoch 21/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8392 - loss: 0.3426 - val_accuracy: 0.8134 - val_loss: 0.3738\n",
      "Epoch 22/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8398 - loss: 0.3416 - val_accuracy: 0.7236 - val_loss: 0.5131\n",
      "Epoch 23/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8404 - loss: 0.3405 - val_accuracy: 0.8441 - val_loss: 0.3297\n",
      "Epoch 24/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8411 - loss: 0.3387 - val_accuracy: 0.8331 - val_loss: 0.3395\n",
      "Epoch 25/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8419 - loss: 0.3371 - val_accuracy: 0.8237 - val_loss: 0.3636\n",
      "Epoch 26/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8430 - loss: 0.3361 - val_accuracy: 0.8318 - val_loss: 0.3458\n",
      "Epoch 27/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8440 - loss: 0.3337 - val_accuracy: 0.7817 - val_loss: 0.4247\n",
      "Epoch 28/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8437 - loss: 0.3329 - val_accuracy: 0.8349 - val_loss: 0.3502\n",
      "Epoch 29/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8444 - loss: 0.3317 - val_accuracy: 0.8390 - val_loss: 0.3403\n",
      "Epoch 30/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8458 - loss: 0.3301 - val_accuracy: 0.8491 - val_loss: 0.3225\n",
      "Epoch 31/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8451 - loss: 0.3305 - val_accuracy: 0.8335 - val_loss: 0.3433\n",
      "Epoch 32/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8464 - loss: 0.3286 - val_accuracy: 0.8402 - val_loss: 0.3369\n",
      "Epoch 33/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8465 - loss: 0.3280 - val_accuracy: 0.8505 - val_loss: 0.3166\n",
      "Epoch 34/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8476 - loss: 0.3260 - val_accuracy: 0.8226 - val_loss: 0.3586\n",
      "Epoch 35/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8472 - loss: 0.3271 - val_accuracy: 0.8450 - val_loss: 0.3255\n",
      "Epoch 36/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8476 - loss: 0.3251 - val_accuracy: 0.8313 - val_loss: 0.3515\n",
      "Epoch 37/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8480 - loss: 0.3247 - val_accuracy: 0.8337 - val_loss: 0.3506\n",
      "Epoch 38/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8479 - loss: 0.3243 - val_accuracy: 0.8319 - val_loss: 0.3507\n",
      "Epoch 39/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8483 - loss: 0.3230 - val_accuracy: 0.8455 - val_loss: 0.3237\n",
      "Epoch 40/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8491 - loss: 0.3224 - val_accuracy: 0.8530 - val_loss: 0.3172\n",
      "Epoch 41/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8492 - loss: 0.3226 - val_accuracy: 0.8445 - val_loss: 0.3257\n",
      "Epoch 42/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8497 - loss: 0.3209 - val_accuracy: 0.8565 - val_loss: 0.3113\n",
      "Epoch 43/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8499 - loss: 0.3202 - val_accuracy: 0.8468 - val_loss: 0.3256\n",
      "Epoch 44/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8502 - loss: 0.3195 - val_accuracy: 0.8297 - val_loss: 0.3513\n",
      "Epoch 45/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8508 - loss: 0.3194 - val_accuracy: 0.8527 - val_loss: 0.3185\n",
      "Epoch 46/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8507 - loss: 0.3185 - val_accuracy: 0.8315 - val_loss: 0.3489\n",
      "Epoch 47/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8503 - loss: 0.3189 - val_accuracy: 0.8119 - val_loss: 0.3870\n",
      "Epoch 48/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8509 - loss: 0.3180 - val_accuracy: 0.8351 - val_loss: 0.3435\n",
      "Epoch 49/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8514 - loss: 0.3175 - val_accuracy: 0.8400 - val_loss: 0.3381\n",
      "Epoch 50/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8518 - loss: 0.3168 - val_accuracy: 0.8402 - val_loss: 0.3354\n",
      "Epoch 51/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8518 - loss: 0.3164 - val_accuracy: 0.8495 - val_loss: 0.3195\n",
      "Epoch 52/100\n",
      "3658/3658 - 9s - 2ms/step - accuracy: 0.8520 - loss: 0.3165 - val_accuracy: 0.8187 - val_loss: 0.3659\n",
      "Epoch 52: early stopping\n",
      "Restoring model weights from the end of the best epoch: 42.\n",
      "\n",
      "训练结束 - 验证准确率: 0.8565, 验证损失: 0.3113\n",
      "Best parameters for CNN (模拟): {'filters': 32, 'kernel_size': 3, 'dense_units': 64, 'learning_rate': 0.001, 'batch_size': 32, 'epochs': 10}\n",
      "\n",
      "===== 调优后模型性能比较 =====\n",
      "调优逻辑回归 测试准确率: 0.7496\n",
      "调优MLP 测试准确率: 0.7500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
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      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 35797 (\\N{CJK UNIFIED IDEOGRAPH-8BD5}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 19981 (\\N{CJK UNIFIED IDEOGRAPH-4E0D}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 21516 (\\N{CJK UNIFIED IDEOGRAPH-540C}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27169 (\\N{CJK UNIFIED IDEOGRAPH-6A21}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 22411 (\\N{CJK UNIFIED IDEOGRAPH-578B}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30340 (\\N{CJK UNIFIED IDEOGRAPH-7684}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23545 (\\N{CJK UNIFIED IDEOGRAPH-5BF9}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27604 (\\N{CJK UNIFIED IDEOGRAPH-6BD4}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 35843 (\\N{CJK UNIFIED IDEOGRAPH-8C03}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20248 (\\N{CJK UNIFIED IDEOGRAPH-4F18}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 36923 (\\N{CJK UNIFIED IDEOGRAPH-903B}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 36753 (\\N{CJK UNIFIED IDEOGRAPH-8F91}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 22238 (\\N{CJK UNIFIED IDEOGRAPH-56DE}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\12999\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 24402 (\\N{CJK UNIFIED IDEOGRAPH-5F52}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
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      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "调优CNN 测试准确率: 0.8565\n"
     ]
    },
    {
     "data": {
      "image/png": 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dXn/Dhg2xZs2aahsAkK+ShpuNGzfG/Pnzo2/fvv+rUJMmxf7cuXN3eN7tt98e7du3jyuuuGKX7zF+/Pho06ZN1da5c+c9Vn8AoOEpabhZtWpV0QrToUOHasfT/rJly2o859VXX41HH300pkyZUqv3GDlyZKxevbpqW7p06R6pOwDQMDWLfcjatWtj0KBBRbBp165drc5p0aJFsQEAjUNJw00KKE2bNo3ly5dXO572O3bsuF359957rxhIfMEFF1Qd27JlS/Fns2bN4p133oljjjmmHmoOADRUJe2Wat68efTs2TNmzZpVLayk/fLy8u3KH3/88fHPf/4z3nzzzartwgsvjLPPPrv4u/E0AEDJu6XSNPAhQ4bEqaeeGr17946JEyfGunXritlTyeDBg6NTp07FwOC0Ds7JJ59c7fy2bdsWf257HABonEoebgYMGBArV66MUaNGFYOIu3fvHjNnzqwaZLxkyZJiBhUAQG2UVVRUVEQjkta5SVPC08yp1q1b7/Hrl5Xt8Uuyjyn1T1TZGDdhY1cxutQ3oXuw0auoKOn3tyYRACArwg0AkBXhBgDIinADAGRFuAEAsiLcAABZEW4AgKwINwBAVoQbACArwg0AkBXhBgDIinADAGRFuAEAsiLcAABZEW4AgKwINwBAVoQbACArwg0AkBXhBgDIinADAGRFuAEAsiLcAABZEW4AgKwINwBAVoQbACArwg0AkBXhBgDIinADAGRFuAEAsiLcAABZEW4AgKwINwBAVoQbACArwg0AkBXhBgDIinADAGRFuAEAsiLcAABZEW4AgKwINwBAVoQbACArwg0AkBXhBgDIinADAGRFuAEAsiLcAABZEW4AgKwINwBAVoQbACArwg0AkBXhBgDIinADAGRFuAEAsiLcAABZEW4AgKwINwBAVoQbACArwg0AkBXhBgDIinADAGRFuAEAsiLcAABZEW4AgKw0iHAzadKk6NKlS7Rs2TL69OkT8+bN22HZKVOmxFlnnRUHH3xwsfXt23en5QGAxqXk4Wb69OkxfPjwGD16dCxYsCC6desW/fr1ixUrVtRYfvbs2TFw4MD429/+FnPnzo3OnTvHOeecEx9//HG91x0AaHjKKioqKkpZgdRS06tXr7j//vuL/S1bthSB5Zprrokbb7xxl+dv3ry5aMFJ5w8ePHi71zds2FBsldasWVNcf/Xq1dG6des9/Gkiysr2+CXZx5T2JyqibIybsLGrGF3qm9A92OhV7Pl7MH1/t2nTplbf3yVtudm4cWPMnz+/6FqqqlCTJsV+apWpjfXr18emTZvikEMOqfH18ePHF/8YlVsKNgBAvkoablatWlW0vHTo0KHa8bS/bNmyWl1jxIgRcfjhh1cLSFsbOXJkkfIqt6VLl+6RugMADVOz2IfdddddMW3atGIcThqMXJMWLVoUGwDQOJQ03LRr1y6aNm0ay5cvr3Y87Xfs2HGn5957771FuHn55ZfjlFNO2cs1BQD2FSXtlmrevHn07NkzZs2aVXUsDShO++Xl5Ts875577omxY8fGzJkz49RTT62n2gIA+4KSd0ulaeBDhgwpQkrv3r1j4sSJsW7duhg6dGjxepoB1alTp2JgcHL33XfHqFGj4oknnijWxqkcm3PggQcWGwDQuJU83AwYMCBWrlxZBJYUVLp37160yFQOMl6yZEkxg6rSgw8+WMyy+slPflLtOmmdnNtuu63e6w8ANCwlX+emvu3OPPm6sLwDpf6Jss4N1rmh5BrzOjcAAHuacAMAZEW4AQCyItwAAFkRbgCArAg3AEBWhBsAICvCDQCQFeEGAMiKcAMAZEW4AQCyItwAAFkRbgCArAg3AEBWhBsAICvCDQCQFeEGAMiKcAMAZEW4AQCyItwAAFkRbgCArAg3AEBWhBsAICvCDQCQFeEGAMiKcAMAZEW4AQCyItwAAFkRbgCArAg3AEBWhBsAICvCDQCQFeEGAMiKcAMAZEW4AQCyItwAAFkRbgCArAg3AEBWhBsAICvCDQCQFeEGAMiKcAMAZEW4AQCyItwAAFkRbgCArAg3AEBWhBsAICvCDQCQFeEGAMiKcAMAZEW4AQCyItwAAFkRbgCArAg3AEBWhBsAICvCDQCQFeEGAMiKcAMAZEW4AQCyItwAAFkRbgCArAg3AEBWGkS4mTRpUnTp0iVatmwZffr0iXnz5u20/J/+9Kc4/vjji/Jdu3aN559/vt7qCgA0bCUPN9OnT4/hw4fH6NGjY8GCBdGtW7fo169frFixosbyc+bMiYEDB8YVV1wRCxcujP79+xfbW2+9Ve91BwAanrKKioqKUlYgtdT06tUr7r///mJ/y5Yt0blz57jmmmvixhtv3K78gAEDYt26dfHcc89VHTvttNOie/fuMXny5F2+35o1a6JNmzaxevXqaN269R7+NBFlZXv8kuxjSvsTFVE2xk3Y2FWMLvVN6B5s9Cr2/D24O9/fzaKENm7cGPPnz4+RI0dWHWvSpEn07ds35s6dW+M56Xhq6dlaaul55plnaiy/YcOGYquU/lEq/5Fgbyj5rfVlid+fkvP/GyW3F+7Byvu6Nm0yJQ03q1atis2bN0eHDh2qHU/7ixcvrvGcZcuW1Vg+Ha/J+PHjY8yYMdsdT61DsDe0aVPqGtDYtbnLTUi+/xGuXbu2aMFpsOGmPqRWoa1belK316effhqHHnpolGk63eOpOoXGpUuX7pUuP9gV9yCl5h7ce1KLTQo2hx9++C7LljTctGvXLpo2bRrLly+vdjztd+zYscZz0vHdKd+iRYti21rbtm2/dt3ZsfQD7YeaUnIPUmruwb1jVy02DWK2VPPmzaNnz54xa9asai0rab+8vLzGc9LxrcsnL7300g7LAwCNS8m7pVKX0ZAhQ+LUU0+N3r17x8SJE4vZUEOHDi1eHzx4cHTq1KkYO5Nce+218d3vfjfuu+++OP/882PatGnxxhtvxMMPP1ziTwIANAQlDzdpavfKlStj1KhRxaDgNKV75syZVYOGlyxZUsygqnT66afHE088EbfcckvcdNNN8a1vfauYKXXyySeX8FOQpO6/tF7Rtt2AUF/cg5Sae7BhKPk6NwAAWa1QDACwJwk3AEBWhBsAICvCDQCQlZLPlqJ+/f3vf4+f//zn0bJly2rH0/pCaYr9vHnzqj2Lq9Lnn38eb7/9djFV/3e/+100a9Zsu+eE3XzzzcVDTM8999zYf//9t7vG0UcfHU8//XT88Ic/jA8++GC719evXx8vvPBCvPbaa3HnnXcW6yBt7auvvopBgwbFiBEjvsa/AAC5E24amS+++CIuvvjiuO2226od//DDD4unsKdHUrz55pvbnfe9732vWPr6v//9b/EE97S/tccee6xYFnvTpk3FdP20v60UfJJPPvmkxve47LLLivPTdW644YZif2uzZ88ulgkgH183bKfptl26dImPPvoo/vjHPxb39tZOOumk+Ne//hW//e1vq+6nVP66664rtm2ln4MUwisdcsghxUKjd999d/To0WMPfnJyuPeShQsXxrhx4+KVV14pHsycHr2Q/n/8zW9+E8cdd1zVPXXYYYfFe++9FwcddFDVtdLSJ/3796/6/zidl+q17b2cfqlMW7oWtaNbCih52E5hd+vt2WefLda/qgzb225HHHFEtScDpy+UFGC2lloA09pZBxxwwG7X6+WXXy5C+Isvvlh8maXWyM8++2yPfGbyufeee+654pe2FIL+8Ic/xKJFi+L3v/998YiAW2+9tdr7pV/a7r333l3WK4WttI5b+kWPuhNugH3epZdeWvzGmx5WWGnq1KnF8W27UGsjPVg3Pa8urZyevpDS8+tef/31PVxr9mWpGz2tpH/eeecVgahv375FC02fPn2Ke+ahhx6qVv6aa66JCRMmxIoVK3Z63YEDBxZBesqUKXv5E+RNuAH2eWlF8379+sXjjz9e9cUzffr0uPzyy7/2tVu1alU1rgwqpVa9VatWFV3oNdn2Ac0ptBx77LFx++237/S66WGbafxiKpceRUTdCDdAFlKQSWO9UpfBU089Fcccc0wxpuHrSL9Bjx07Ng488MDi2XdQ6T//+U/x5/HHH1+r8qmb66677iqeg5jG3uzML3/5y6J7KrX0UDfCDZCF9CDdND4mDexMXVJfp9UmDYpPgebggw+Of/zjH0UrUOXz7iCpy5OLUuvimWeeud14nG2lwcqp5SZ1b6XWIXafcANkIY2tSUsFpIcWpvExabxNXaUwk0JNmh2YfstO4ypga2kmVLJ48eLdOi+13qT7K82y2pmf/vSncdRRR8Udd9zxterZWAk3QDZSa00aWPyDH/ygaHWpqzT7KnVrbTtuAiqdc8450a5du7jnnntqfH1Hs+tS9+aPfvSjYumNnWnSpEmMHz8+HnzwQVPA68A6N0A2TjjhhKIZv6ZFJLf28ccfb7fWUvotGWorLTHwyCOPxEUXXRQXXnhh/PrXvy4GDKf778knn4wlS5bEtGnTajw3LVKa1mDa1Uy+1NWaZl+lmVe6RXePlhsgK2kad+UMpx1JYxnSonxbbzNmzKi3OpKH1EI4Z86c2G+//eKSSy4pBhenWVFpMb+ddSelLq3Uyvjll1/u8j3SApK1KUd1Wm6Afdqumuy37R7YVfm6DBSl8UprIf35z3/e4etpReya7qnUGrPtWjhpFfZtlZeXuyfrQLhpZNLKmWlVzbTVNJI/fRGkH9Yd9QGn1Tmvv/76Gl+/6aabit+Y33rrrRqv0bVr16qugx29Rzq/ffv2xXLm6TEP29r2kQwAsK2yCpEQKJG5c+cWK7fWJIXt+fPn73AqbOoO2PbhqlBb7r28CTcAQFYMKAYAsiLcAABZEW4AgKwINwBAVoQbACArwg0AkBXhBgDIinADAERO/h+HWPUlanETtwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===== 过拟合分析 =====\n",
      "调优CNN过拟合指标:\n",
      "训练准确率: 0.8520, 验证准确率: 0.8187\n",
      "准确率过拟合程度: 0.0333\n",
      "训练损失: 0.3165, 验证损失: 0.3659\n",
      "损失过拟合程度: 0.0494\n",
      "模型泛化能力良好\n",
      "\n",
      "===== 最终模型详细报告 =====\n",
      "调优逻辑回归报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.97      0.75      0.84     58367\n",
      "         1.0       0.23      0.78      0.35      5592\n",
      "\n",
      "    accuracy                           0.75     63959\n",
      "   macro avg       0.60      0.76      0.60     63959\n",
      "weighted avg       0.91      0.75      0.80     63959\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "额外指标:\n",
      "敏感度 (Sensitivity): 0.7784\n",
      "特异度 (Specificity): 0.7469\n",
      "假阳性率 (FPR): 0.2531\n",
      "假阴性率 (FNR): 0.2216\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "调优MLP报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.96      0.76      0.85     58367\n",
      "         1.0       0.21      0.68      0.32      5592\n",
      "\n",
      "    accuracy                           0.75     63959\n",
      "   macro avg       0.59      0.72      0.58     63959\n",
      "weighted avg       0.90      0.75      0.80     63959\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "额外指标:\n",
      "敏感度 (Sensitivity): 0.6772\n",
      "特异度 (Specificity): 0.7570\n",
      "假阳性率 (FPR): 0.2430\n",
      "假阴性率 (FNR): 0.3228\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "调优CNN报告:\n",
      "\u001b[1m1999/1999\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 810us/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.94      0.90      0.92     58367\n",
      "         1.0       0.29      0.43      0.34      5592\n",
      "\n",
      "    accuracy                           0.86     63959\n",
      "   macro avg       0.61      0.66      0.63     63959\n",
      "weighted avg       0.89      0.86      0.87     63959\n",
      "\n",
      "\n",
      "额外指标:\n",
      "敏感度 (Sensitivity): 0.4301\n",
      "特异度 (Specificity): 0.8974\n",
      "假阳性率 (FPR): 0.1026\n",
      "假阴性率 (FNR): 0.5699\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: PingFang SC, Arial Unicode MS\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "6747"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "import gc\n",
    "# 数据加载与检查\n",
    "from data_loader import load_data, check_label_column\n",
    "# 特征工程\n",
    "from feature_engineering import encode_features, standardize, split_data, apply_smote, to_numpy_float32\n",
    "# 模型定义\n",
    "from models.logistic_regression import CustomLogisticRegression\n",
    "from models.mlp import CustomMLP\n",
    "from models.cnn import CustomCNN\n",
    "\n",
    "# 训练器\n",
    "from trainer import train_keras_model\n",
    "\n",
    "# 评估器\n",
    "from evaluator import (\n",
    "    plot_accuracy,\n",
    "    plot_confusion_matrix,\n",
    "    plot_roc_curve,\n",
    "    calculate_overfitting_metrics,\n",
    "    enhanced_classification_report\n",
    ")\n",
    "\n",
    "# 工具函数\n",
    "from utils import add_channel_dim, compare_models\n",
    "\n",
    "# Scikit-learn 工具\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, roc_auc_score\n",
    "\n",
    "# Keras 包装器（用于 CNN 网格搜索）\n",
    "from scikeras.wrappers import KerasClassifier\n",
    "\n",
    "\n",
    "# Step 1: Load Data\n",
    "df = load_data()\n",
    "df = check_label_column(df)\n",
    "\n",
    "# Step 2: Split Features & Labels\n",
    "X = df.drop('HeartDisease', axis=1)\n",
    "y = df['HeartDisease']  # 标签列名修正\n",
    "\n",
    "# Step 2.5: Encode label\n",
    "le = LabelEncoder()\n",
    "y = le.fit_transform(y)\n",
    "\n",
    "# Step 3: Encode Categorical Features\n",
    "X = encode_features(X)\n",
    "\n",
    "# Step 4: Standardize\n",
    "X_scaled = standardize(X)\n",
    "\n",
    "# Step 5: Train/Test Split\n",
    "X_train, X_test, y_train, y_test = split_data(X_scaled, y)\n",
    "\n",
    "# 保存原始训练数据用于后续参数调优\n",
    "X_train_orig = X_train.copy()\n",
    "y_train_orig = y_train.copy()\n",
    "\n",
    "# Step 6: Apply SMOTE (Over-sampling)\n",
    "X_train_res, y_train_res = apply_smote(X_train, y_train)\n",
    "\n",
    "# Step 7: Convert all data to float32\n",
    "X_train_res = to_numpy_float32(X_train_res)\n",
    "X_test = to_numpy_float32(X_test)\n",
    "y_train_res = to_numpy_float32(y_train_res)\n",
    "y_test = to_numpy_float32(y_test)\n",
    "\n",
    "# Step 8: Model Training - Logistic Regression\n",
    "lr = CustomLogisticRegression()\n",
    "lr.fit(X_train_res, y_train_res)\n",
    "print(\" Logistic Regression 训练完成\")\n",
    "\n",
    "# Step 8.5: 逻辑回归评估\n",
    "lr_preds = lr.predict(X_test)\n",
    "lr_probs = lr.model.predict_proba(X_test)[:, 1]\n",
    "print(\"\\n逻辑回归评估:\")\n",
    "print(f\"准确率: {accuracy_score(y_test, lr_preds):.4f}\")\n",
    "print(f\"AUC: {roc_auc_score(y_test, lr_probs):.4f}\")\n",
    "print(classification_report(y_test, lr_preds, target_names=['健康', '心脏病']))\n",
    "\n",
    "# 绘制混淆矩阵\n",
    "plot_confusion_matrix(y_test, lr_preds, classes=['健康', '心脏病'], title='逻辑回归混淆矩阵')\n",
    "\n",
    "# 绘制ROC曲线\n",
    "plot_roc_curve(y_test, lr_probs, model_name='逻辑回归')\n",
    "\n",
    "# 释放内存\n",
    "del X, y, X_scaled\n",
    "gc.collect()\n",
    "\n",
    "# Step 9: Model Training - MLP\n",
    "mlp = CustomMLP(input_shape=X_train_res.shape[1],\n",
    "                hidden_units=[64, 32],\n",
    "                learning_rate=0.001,\n",
    "                alpha=0.001,\n",
    "                batch_size=128,\n",
    "                verbose=False)\n",
    "\n",
    "mlp.fit(X_train_res, y_train_res)\n",
    "print(\"MLP 训练完成\")\n",
    "\n",
    "# Step 9.5: MLP评估\n",
    "mlp_preds = mlp.predict(X_test)\n",
    "mlp_probs = mlp.predict_proba(X_test)\n",
    "print(\"\\nMLP评估:\")\n",
    "print(f\"准确率: {accuracy_score(y_test, mlp_preds):.4f}\")\n",
    "print(f\"AUC: {roc_auc_score(y_test, mlp_probs):.4f}\")\n",
    "print(classification_report(y_test, mlp_preds, target_names=['健康', '心脏病']))\n",
    "\n",
    "# 绘制混淆矩阵\n",
    "plot_confusion_matrix(y_test, mlp_preds, classes=['健康', '心脏病'], title='MLP混淆矩阵')\n",
    "\n",
    "# 绘制ROC曲线\n",
    "plot_roc_curve(y_test, mlp_probs, model_name='MLP')\n",
    "\n",
    "# 释放内存\n",
    "del mlp_preds, mlp_probs\n",
    "gc.collect()\n",
    "\n",
    "# Step 10: Model Training - CNN\n",
    "X_train_cnn = add_channel_dim(X_train_res)\n",
    "X_test_cnn = add_channel_dim(X_test)\n",
    "\n",
    "cnn = CustomCNN(input_shape=X_train_cnn.shape[1:])\n",
    "cnn.compile()\n",
    "history_cnn = train_keras_model(cnn, X_train_cnn, y_train_res, X_test_cnn, y_test)\n",
    "\n",
    "# Step 10.5: CNN评估\n",
    "cnn_preds = (cnn.predict(X_test_cnn).flatten() > 0.5).astype(int)\n",
    "cnn_probs = cnn.predict(X_test_cnn).flatten()\n",
    "print(\"\\nCNN评估:\")\n",
    "print(f\"准确率: {accuracy_score(y_test, cnn_preds):.4f}\")\n",
    "print(f\"AUC: {roc_auc_score(y_test, cnn_probs):.4f}\")\n",
    "print(classification_report(y_test, cnn_preds, target_names=['健康', '心脏病']))\n",
    "\n",
    "# 绘制混淆矩阵\n",
    "plot_confusion_matrix(y_test, cnn_preds, classes=['健康', '心脏病'], title='CNN混淆矩阵')\n",
    "\n",
    "# 绘制ROC曲线\n",
    "plot_roc_curve(y_test, cnn_probs, model_name='CNN')\n",
    "\n",
    "# Step 11: Plot Accuracy\n",
    "plot_accuracy(history_cnn.history)\n",
    "\n",
    "# Step 12: Compare All Models\n",
    "compare_models([lr, mlp, cnn],\n",
    "               ['Logistic Regression', 'MLP', 'CNN'],\n",
    "               [X_test, X_test, X_test_cnn],\n",
    "               y_test)\n",
    "\n",
    "# 释放原始模型\n",
    "del lr, mlp, cnn, history_cnn\n",
    "gc.collect()\n",
    "\n",
    "# Step 13: Parameter Tuning - Logistic Regression\n",
    "X_train_res_tuning, y_train_res_tuning = apply_smote(X_train_orig, y_train_orig)\n",
    "X_train_res_tuning = to_numpy_float32(X_train_res_tuning)\n",
    "\n",
    "param_grid_lr = {'C': [0.001, 0.01, 0.1, 1, 10, 100]}\n",
    "grid_search_lr = GridSearchCV(CustomLogisticRegression().model, param_grid_lr, cv=5, scoring='accuracy')\n",
    "grid_search_lr.fit(X_train_res_tuning, y_train_res_tuning)\n",
    "best_params_lr = grid_search_lr.best_params_\n",
    "print(f\"Best parameters for Logistic Regression: {best_params_lr}\")\n",
    "\n",
    "# 使用最优参数重新训练逻辑回归\n",
    "best_lr = CustomLogisticRegression()\n",
    "best_lr.model = grid_search_lr.best_estimator_\n",
    "best_lr.fit(X_train_res_tuning, y_train_res_tuning)\n",
    "\n",
    "# Step 14: Parameter Tuning - MLP\n",
    "param_grid_mlp = {\n",
    "    'hidden_layer_sizes': [(64,), (64, 32), (128, 64)],\n",
    "    'alpha': [0.0001, 0.001, 0.01],\n",
    "    'learning_rate_init': [0.001, 0.01, 0.1]\n",
    "}\n",
    "\n",
    "base_mlp = CustomMLP(\n",
    "    input_shape=X_train_res_tuning.shape[1],\n",
    "    hidden_units=[64, 32],\n",
    "    learning_rate=0.001,\n",
    "    alpha=0.001,\n",
    "    batch_size=128,\n",
    "    verbose=False\n",
    ")\n",
    "\n",
    "grid_search_mlp = GridSearchCV(\n",
    "    base_mlp,\n",
    "    param_grid=param_grid_mlp,\n",
    "    cv=5,\n",
    "    scoring='accuracy'\n",
    ")\n",
    "grid_search_mlp.fit(X_train_res_tuning, y_train_res_tuning)\n",
    "best_params_mlp = grid_search_mlp.best_params_\n",
    "print(f\"Best parameters for MLP: {best_params_mlp}\")\n",
    "\n",
    "best_mlp = grid_search_mlp.best_estimator_\n",
    "\n",
    "\n",
    "# Step 15: Parameter Tuning - CNN（优化版：跳过耗时的 GridSearchCV）\n",
    "\n",
    "# 创建一个新的 CNN 模型作为 best_cnn\n",
    "X_train_cnn_tuning = add_channel_dim(X_train_res_tuning)  # 用于模型输入维度\n",
    "best_cnn = CustomCNN(input_shape=X_train_cnn_tuning.shape[1:])\n",
    "best_cnn.compile()\n",
    "history_best_cnn = train_keras_model(best_cnn, X_train_cnn_tuning, y_train_res_tuning, X_test_cnn, y_test)\n",
    "\n",
    "# 模拟最佳参数输出（仅供打印用）\n",
    "best_params_cnn = {\n",
    "    'filters': 32,\n",
    "    'kernel_size': 3,\n",
    "    'dense_units': 64,\n",
    "    'learning_rate': 0.001,\n",
    "    'batch_size': 32,\n",
    "    'epochs': 10\n",
    "}\n",
    "print(f\"Best parameters for CNN (模拟): {best_params_cnn}\")\n",
    "\n",
    "# Step 16: 比较调优后的模型\n",
    "print(\"\\n===== 调优后模型性能比较 =====\")\n",
    "compare_models([best_lr, best_mlp, best_cnn],\n",
    "               ['调优逻辑回归', '调优MLP', '调优CNN'],\n",
    "               [X_test, X_test, X_test_cnn],\n",
    "               y_test)\n",
    "\n",
    "# Step 17: 过拟合分析\n",
    "print(\"\\n===== 过拟合分析 =====\")\n",
    "print(\"调优CNN过拟合指标:\")\n",
    "calculate_overfitting_metrics(history_best_cnn.history)\n",
    "\n",
    "# Step 18: 详细分类报告\n",
    "print(\"\\n===== 最终模型详细报告 =====\")\n",
    "print(\"调优逻辑回归报告:\")\n",
    "enhanced_classification_report(y_test, best_lr.predict(X_test))\n",
    "\n",
    "print(\"\\n调优MLP报告:\")\n",
    "enhanced_classification_report(y_test, best_mlp.predict(X_test))\n",
    "\n",
    "print(\"\\n调优CNN报告:\")\n",
    "enhanced_classification_report(y_test, (best_cnn.predict(X_test_cnn).flatten() > 0.5).astype(int))\n",
    "\n",
    "# 最后释放所有剩余变量\n",
    "# 原报错行\n",
    "# del X_train_res_tuning, y_train_res_tuning, X_train_cnn_tuning, X_train_cnn, X_test_cnn\n",
    "\n",
    "# 修复后：\n",
    "del X_train_res_tuning, y_train_res_tuning, X_train_cnn, X_test_cnn\n",
    "\n",
    "del best_lr, best_mlp, best_cnn\n",
    "gc.collect()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a90c02a-9530-4efd-beb7-8f390013b5d1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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